5G and 6G Communication Technologies 9781774691823

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5G and 6G Communication Technologies

5G and 6G Communication Technologies

Edited by: Zoran Gacovski

www.arclerpress.com

5G and 6G Communication Technologies Zoran Gacovski

Arcler Press 224 Shoreacres Road Burlington, ON L7L 2H2 Canada www.arclerpress.com Email: [email protected] HERRN(GLWLRQ2 ISBN: (HERRN)

This book contains information obtained from highly regarded resources. Reprinted material sources are indicated. Copyright for individual articles remains with the authors as indicated and published under Creative Commons License. A Wide variety of references are listed. Reasonable efforts have been made to publish reliable data and views articulated in the chapters are those of the individual contributors, and not necessarily those of the editors or publishers. Editors or publishers are not responsible for the accuracy of the information in the published chapters or consequences of their use. The publisher assumes no responsibility for any damage or grievance to the persons or property arising out of the use of any materials, instructions, methods or thoughts in the book. The editors and the publisher have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission has not been obtained. If any copyright holder has not been acknowledged, please write to us so we may rectify. Notice: Registered trademark of products or corporate names are used only for explana       

© 2022 Arcler Press ISBN: 978-1-77469-182-3 (Hardcover)

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ABOUT THE EDITOR

Dr. Zoran Gacovski has earned his PhD degree at Faculty of Electrical engineering, Skopje. His research interests include Intelligent systems and Software engineering, fuzzy systems, graphical models (Petri, Neural and Bayesian networks), and IT security. He has published over 50 journal and conference papers, and he has been reviewer of renowned Journals. Currently, he is a professor in Computer Engineering at European University, Skopje, Macedonia.

TABLE OF CONTENTS

List of Contributors ......................................................................................xiii List of Abbreviations .................................................................................... xix Preface.................................................................................................... ....xxi

Section 1: Telecommunication, Antenna and Bandwidth aspects of 5G Chapter 1

Mobile Communication Through 5G Technology (Challenges and Requirements) ................................................................. 3 Abstract ..................................................................................................... 3 Introduction ............................................................................................... 4 Challenges in Migration from 4G to 5G ..................................................... 5 Key Terms of 5G Technology ...................................................................... 6 5G Technology Requirements .................................................................... 7 5G Technology Features ............................................................................. 7 Conclusion and Future Scopes ................................................................... 9 References ............................................................................................... 10

Chapter 2

A Review in the Core Technologies of 5G: Device-to-Device Communication, Multi-Access Edge Computing and Network Function Virtualization ........................................................................... 11 Abstract ................................................................................................... 12 Introduction ............................................................................................. 12 Background ............................................................................................. 13 Device-to-Device Communication .......................................................... 15 MEC ...................................................................................................... 21 Network Function Virtualization .............................................................. 30 Conclusions ............................................................................................. 40 References ............................................................................................... 41

Chapter 3

Design of a Multiband Patch Antenna for 5G Communication Systems ......................................................................... 43 Abstract ................................................................................................... 43 Introduction ............................................................................................. 44 Antenna Design ....................................................................................... 45 Results and Analysis................................................................................. 47 Conclusions and Future Works ................................................................ 55 Acknowledgements ................................................................................. 56 References ............................................................................................... 57

Chapter 4

Wideband Reconfigurable Millimeter-Wave Linear Array Antenna Using Liquid Crystal for 5G Networks ...................................... 59 Abstract ................................................................................................... 59 Introduction ............................................................................................. 60 Research Method ..................................................................................... 62 Results and Analysis................................................................................. 65 Conclusion .............................................................................................. 72 References ............................................................................................... 73

Chapter 5

FBMC vs OFDM Waveform Contenders for 5G Wireless Communication System........................................................................... 77 Abstract ................................................................................................... 77 Introduction ............................................................................................. 78 Differences between OFDM and FBMC Multi Carrier Techniques ............ 79 Transmultiplexer Configuration of Filter Bank Multi Carrier...................... 81 Simulation Results ................................................................................... 86 Conclusion .............................................................................................. 88 Acknowledgements ................................................................................. 89 References ............................................................................................... 90

Section 2: Business Solutions Enabled by 5G Technology Chapter 6

The Roles of 5G Mobile Broadband in the Development of IoT, Big Data, Cloud and SDN........................................................................ 93 Abstract ................................................................................................... 93 Introduction ............................................................................................. 94 The Roles of 5G, IoT, Big Data, Cloud, and SDN till 2020 ....................... 96 Technical Relationships among IoT, Big Data, Cloud, & SDN in 5G Era ... 96

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Ongoing Programs and Applications at National Chiao Tung University (NCTU) ......................................... 99 Conclusion ............................................................................................ 109 Acknowledgements ............................................................................... 109 References ............................................................................................. 110 Chapter 7

Planning and Profit Sharing in Overlay WiFi and LTE Systems toward 5G Networks ............................................................... 113 Abstract ................................................................................................. 113 Introduction ........................................................................................... 114 Overlay LTE/WiFi Network Model.......................................................... 116 Problem Formulation ............................................................................. 118 WiFi Dimensioning Method ................................................................... 119 Profit Estimation..................................................................................... 122 Simulation Results and performance Evaluation ..................................... 127 Conclusions ........................................................................................... 132 References ............................................................................................. 133

Chapter 8

Construction of Enterprise 5G Business Ecosystem: Case Study of Huawei ........................................................................... 135 Abstract ................................................................................................. 135 Introduction ........................................................................................... 136 Research Design .................................................................................... 137 Case Introduction .................................................................................. 139 Case Analysis ......................................................................................... 142 Discussion and Enlightenment ............................................................... 148 Research Conclusions and Theoretical Contributions ............................. 155 References ............................................................................................. 157

Chapter 9

5G New Radio Prototype Implementation Based on SDR ..................... 161 Abstract ................................................................................................. 161 Introduction ........................................................................................... 162 Related Work ......................................................................................... 165 5G System Architecture and Scenario .................................................... 168 Experimental Results and Analysis ......................................................... 184 Conclusion ............................................................................................ 191 References ............................................................................................. 192 ix

Section 3: 5G Applications in Different Scenarios Chapter 10 The Roles of 5G Mobile Broadband in the Development of IoT, Big Data, Cloud and SDN .......................................................... 197 Abstract ................................................................................................. 197 Introduction ........................................................................................... 198 DME in Past ........................................................................................... 199 5G Communication ............................................................................... 201 Prospect................................................................................................. 202 NOTES .................................................................................................. 205 References ............................................................................................. 206 Chapter 11 Research on the Innovation Path of Logistics Formats Based on 5G Technology....................................................................... 209 Abstract ................................................................................................. 209 Introduction ........................................................................................... 210 Research Design .................................................................................... 210 Analysis on the Innovation Path of China’s Logistics Format in the 5G Era .................................................................... 213 Conclusions ........................................................................................... 215 NOTES .................................................................................................. 216 References ............................................................................................. 217 Chapter 12 Limiting Energy Consumption by Decreasing Packets Retransmissions in 5G Network ............................................................ 219 Abstract ................................................................................................. 219 Introduction ........................................................................................... 220 Multipath TCP........................................................................................ 221 OFN for 5G Network ............................................................................. 227 OFN for Errors Prediction ...................................................................... 232 Conclusions ........................................................................................... 237 References ............................................................................................. 238 Chapter 13 Effective Packet Number for 5G IM WeChat Application at Early Stage Traffic Classification ........................................................... 241 Abstract ................................................................................................. 241 Introduction ........................................................................................... 242

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Related Work ......................................................................................... 243 Datasets ................................................................................................. 245 Study Framework ................................................................................... 247 Methodology ......................................................................................... 249 Experimental Results And Analysis ......................................................... 256 Analysis And Discussion ........................................................................ 276 Conclusion ............................................................................................ 277 References ............................................................................................. 278 Chapter 14 Software Defined Network (SDN) and OpenFlow Protocol in 5G Network ........................................................................ 283 Abstract ................................................................................................. 283 Introduction ........................................................................................... 284 Software Defined Network (SDN) .......................................................... 285 The Handover ........................................................................................ 288 Software Defined Network (SDN) and OpenFlow for 5G ....................... 289 Results ................................................................................................... 294 Discussion ............................................................................................. 295 Conclusions ........................................................................................... 296 References ............................................................................................. 298

Section 4: 5G Applications in Different Scenarios Chapter 15 The Shift to 6G Communications: Vision and RequireMents ................ 301 Abstract ................................................................................................. 302 Introduction ........................................................................................... 302 6G System Architecture ......................................................................... 307 Network Dimensions ............................................................................. 314 Potential Technologies ........................................................................... 318 6G application....................................................................................... 323 Key performance indicators (KPIs) .......................................................... 325 Research Challenges and Directions ...................................................... 330 Conclusion ............................................................................................ 332 References ............................................................................................. 333

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Chapter 16 A Semidynamic Bidirectional Clustering Algorithm for Downlink Cell-Free Massive Distributed Antenna System ..................... 343 Abstract ................................................................................................. 343 Introduction ........................................................................................... 344 System Model ........................................................................................ 346 Semidynamic Bidirectional Clustering Algorithm ................................... 349 Numerical Results.................................................................................. 355 Conclusion ............................................................................................ 360 Acknowledgments ................................................................................. 360 References ............................................................................................. 361 Chapter 17 Resource Allocation for SWIPT Systems with Nonlinear Energy Harvesting Model ...................................................................... 363 Abstract ................................................................................................. 363 Introduction ........................................................................................... 364 System Model ........................................................................................ 366 Design of Resource Allocation Optimization Algorithm ......................... 368 Numerical Results.................................................................................. 373 Conclusions ........................................................................................... 377 Acknowledgments ................................................................................. 378 References ............................................................................................. 379 Chapter 18 A Resource Allocation Scheme with Delay Optimization Considering mmWave Wireless Networks ............................................ 383 Abstract ................................................................................................. 383 Introduction ........................................................................................... 384 Transmission System Model ................................................................... 385 Resource Allocation With Delay Optimization ....................................... 388 Simulations and Results ......................................................................... 391 Conclusions ........................................................................................... 401 Acknowledgements ............................................................................... 401 References ............................................................................................. 402 Index ..................................................................................................... 405

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LIST OF CONTRIBUTORS Naseer Hwaidi Alkhazaali Signal School of Electronic Information and Communication Engineering, Huazhong University of Science and Technology, Wuhan, China. Ministry of Communication, Iraqi Telecommunication and Post Company, Baghdad, Iraq. Raed Abduljabbar Aljiznawi Signal School of Electronic Information and Communication Engineering, Huazhong University of Science and Technology, Wuhan, China. Ministry of Communication, Iraqi Telecommunication and Post Company, Baghdad, Iraq. Saba Qasim Jabba Signal School of Electronic Information and Communication Engineering, Huazhong University of Science and Technology, Wuhan, China. Electrical Engineering Department, University of Baghdad, Baghdad, Iraq. Dheyaa Jasim Kadhim Electrical Engineering Department, University of Baghdad, Baghdad, Iraq. Ruixuan Tu Sino-U.S. Program Attached Middle School to Jiangxi Normal University, Nanchang, China. Ruxun Xiang School of Communication Engineering, Chengdu University of Information Technology, Chengdu, China. Yihan Mei UoG-UESTC Joint School University of Electronic Science and Technology of China, Chengdu, China. Yihan Mei Lemania Altdorf Ecole, Shanghai, China.

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Atik Mahabub Department of Electronics and Communication Engineering, KUET, Khulna, Bangladesh. Md. Mostafizur Rahman Department of Electronics and Communication Engineering, KUET, Khulna, Bangladesh. Md. Al-Amin Department of Electrical and Electronic Engineering, KUET, Khulna, Bangladesh. Md. Sayedur Rahman Department of Electronics and Communication Engineering, KUET, Khulna, Bangladesh. Md. Masud Rana Department of Electronics and Communication Engineering, KUET, Khulna, Bangladesh. Ali El Hajj Hassan Ecole Doctorale des Sciences et de la Technologie (EDST), Lebanese University (LU), Beirut, Lebanon. Ecole Supérieure d’Ingénieurs de Beyrouth (ESIB), Université Saint-Joseph (USJ), Beirut, Lebanon. Najib Fadlallah Ecole Doctorale des Sciences et de la Technologie (EDST), Lebanese University (LU), Beirut, Lebanon. Mohammad Rammal Ecole Doctorale des Sciences et de la Technologie (EDST), Lebanese University (LU), Beirut, Lebanon. Georges Zakka El Nashef Ecole Supérieure d’Ingénieurs de Beyrouth (ESIB), Université Saint-Joseph (USJ), Beirut, Lebanon. Elias Rachid Ecole Supérieure d’Ingénieurs de Beyrouth (ESIB), Université Saint-Joseph (USJ), Beirut, Lebanon. Parnika Kansal Department of Electronics, School of Engineering, Harcourt Butler Technical University (HBTU), Kanpur, India xiv

Ashok Kumar Shankhwar Department of Electronics, School of Engineering, Harcourt Butler Technical University (HBTU), Kanpur, India Bao-Shuh Paul Lin Microelectronics & Information Research Center, National Chiao Tung University, Taiwan. College of Computer Science, National Chiao Tung University, Taiwan. Fuchun Joseph Lin Microelectronics & Information Research Center, National Chiao Tung University, Taiwan. College of Computer Science, National Chiao Tung University, Taiwan. Li-Ping Tung Microelectronics & Information Research Center, National Chiao Tung University, Taiwan. Danielle Saliba Signal and Communication, Mines Telecom Atlantique, Brest, France. Rodrigue Imad Mechatronics, University of Balamand, Al Kurah, Lebanon. Sebastien Houcke Signal and Communication, Mines Telecom Atlantique, Brest, France. Bachar El Hassan Telecommunication and Networking, Lebanese University, Tripoli, Lebanon. Yan Li School of Business Administration, University of Science and Technology Liaoning, Anshan, China. Tianzhu Li School of Business Administration, University of Science and Technology Liaoning, Anshan, China. Lama Y. Hosni Department of Electronics & Communication Engineering, Misr International University, Cairo, Egypt. Ahmed Y. Farid Department of Electronics & Communication Engineering, Misr International University, Cairo, Egypt. xv

Abdelrahman A. Elsaadany Department of Electronics & Communication Engineering, Misr International University, Cairo, Egypt. Mahammad A. Safwat National Telecommunication Institute, Cairo, Egypt. Jiaqi Xu Burn and Plastic Surgery, The Fifth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China. Yujie Cui Rheumatology, The Fifth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China. Xinfeng Huang Burn and Plastic Surgery, The Fifth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China. Shifu Mo Burn and Plastic Surgery, The Fifth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China. Liangyue Wang Burn and Plastic Surgery, The Fifth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China. Guangjin Su Burn and Plastic Surgery, The Fifth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China. Yong Cheng Burn and Plastic Surgery, The Fifth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China. Yanan Yan Shanghai University, Shanghai, China.  Apiecionek Institute of Technology, Kazimierz Wielki University, Bydgoszcz, Poland

xvi

Muhammad Shafiq School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China Xiangzhan Yu School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China Khaled Alghamdi School of Electrical and Data Engineering, University of Technology, Sydney, Australia. Robin Braun College of Computer Science and Information Technology, Al Baha University, Al Baha, Saudi Arabia. Muhammad Waseem Akhtar School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad, Pakistan Syed Ali Hassan School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad, Pakistan Rizwan Ghafar Wi-Fi division of Broadcom, San Jose, CA, USA Haejoon Jung Department of Information and Telecommunication Engineering, Incheon National University, Incheon, 22012, Korea Sahil Garg Electrical Engineering Department, École de Technologie Supérieure, Montréal, QC, H3C 1K3, Canada M. Shamim Hossain Department of Software Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, 11543, Saudi Arabia PanpanQian School of Information Science and Technology, Nantong University, Nantong 226019, China

xvii

Huan Zhao School of Information Science and Technology, Nantong University, Nantong 226019, China Yanmin Zhu School of Information Science and Technology, Nantong University, Nantong 226019, China Qiang Sun School of Information Science and Technology, Nantong University, Nantong 226019, China Nantong Research Institute for Advanced Communication Technologies, Nantong 226019, China Yifan Hu School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, China Mingang Liu School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, China Yizhi Feng School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, China Marcus V. G. Ferreira Instituto de Informática, Universidade Federal de Goiás, Goiania, Goiás, Brasil Flávio H. T. Vieira Instituto de Informática, Universidade Federal de Goiás, Goiania, Goiás, Brasil Escola de EngenhariaElétrica, Mecanica e de Computação, Universidade Federal de Goiás, Goiania, Goiás, Brasil Marcos N. L. Carvalho Escola de EngenhariaElétrica, Mecanica e de Computação, Universidade Federal de Goiás, Goiania, Goiás, Brasil

xviii

LIST OF ABBREVIATIONS APs

Access Points

AWGN

Additive white Gaussian noise

AMPS

Advanced mobile phone system

AFB

Analysis filter bank

API

Application programming interface

AR

Augmented Reality

BBU

Building Baseband Unit

CO

Central Office

CQI

Channel Quality feedback Information

CSI

Channel State Information

CCA

Clear Channel Assessment

CDMA

Code division multiple access

DPI

Deep packet inspection

D2D

Device-to-Device communication

DSPs

Digital Signal Processors

DME

Distance medical education

EW

Electromagnetic waves

eMBB

Enhanced Mobile Broadband

FCC

Federal Communications Commission

FPGAs

Field Programmable Gate Arrays

FBMC

Filter bank multicarrier

FIR

Finite impulse response

GSM

Global System for Mobile

GPPs

General purpose processors

HetNets

Heterogeneous Networks

HAPS

High altitude stratospheric platform station

HARQ

Hybrid Automatic Repeat Request

ICT

Information and communication technology

IRSs

Intelligent Reflecting Surfaces

ITU

International Telecommunication Union

ISI

Inter symbol interference

IFFT

Inverse fast Fourier transformation

KPIs

Key Performance Indicators

LC

Liquid crystal

LTE

Long Term Evolution

LDPC

Low-Density parity-check

MTC

Machine-Type-Communications

mMTC

Massive machine type communications

MMSE

Minimum mean square error

NPR

Nearly perfect reconstruction

NFV

Network function virtualization

NSA

Non-Standalone

OWA

Open Wireless Architecture

OAM

Orbital angular momentum

OFDM

Orthogonal frequency division multiplexing

PDCP

Packet data convergence protocol

PSO

Particle Swarm Optimization

PMD

Physical Medium Dependent

OQAM

Quadrature Amplitude Modulation

RAN

Radio Access Network

RAT

Radio Access Technology

SNR

Signal-to-noise ratio

SWIPT

Simultaneous wireless information and power transfer

SDR

Software-Defined Radio

3GPP

Third Generation Partnership Project

TFT

Traffic Flow Template

URLLC

Ultra Reliable Low Latency Communication

UMTS

Universal Mobile Telecommunication System

UEs

User Equipments

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PREFACE

The fifth generation mobile networksare the latest standardized generation of mobile systems, which increase the speeds provided by 4G by 10 times, with the help of interfaces for so-called new radio.The 5G networks use new frequency bands which provide new opportunities for service development in different market segments.The first 5G standard, 3GPP Release 15 was released in June 2019. The 2020 decade will definitely mark the 5G network infrastructure. It can be noticed that each new generation of mobile network improves two important features, which are increased data rate for data transfer and reduced latency (packet latency). The trend of improvement of these two key parameters will not change in the near future, and it is expected to be the same in the upcoming sixth generation mobile network (6G). In this book we give an overview of the key development parameters of these new technologies of the mobile networks. 5G/6G includes New Radio (NR) in frequency bands below 6 GHz, as well as in millimeter bands above 24 GHz. 5G/6G innovations, such as network virtualizationand network software, i.e. 5G Next Generation Core and 5G NR access networks - are based on different functions in the user and the control plane. This separation provides the basis for network partitioning, with three main service types defined for the 5G era: enhanced mobile broadband (eMBB), mass-to-machine (mMTC) and highlatency communications (URLLC), which is realized through special network layers, as logically separated network partitions. Considering the 5G business aspects, the initial driver of 5G will be eMBB, followed by mMTC, and the last service type to be implemented that has the highest performance requirements is URLLC. These services are expected to transform industries and raise the level of digitalization of societies. This edition covers different topics from 5G and 6G mobile technologies, including: telecommunication, antenna and bandwidth aspects of 5G, business solutions enabled by 5G technology, different application scenarios of 5G, and topics from the 6G technology. Section 1 focuses on telecommunication, antenna and bandwidth aspects of 5G, describing mobile communication through 5G technology, a review in the core technologies of 5G: device-to-device communication, multi-access edge computing and network function virtualization, design of a multiband patch antenna for 5G communication systems, wideband reconfigurable millimeter-wave linear array antenna using liquid crystal for 5G networks, and FBMC vs OFDM waveform contenders for 5G wireless communication system.

Section 2 focuses on business solutions enabled by 5G technology, describing the roles of 5G mobile broadband in the development of IoT, big data, cloud and SDN, planning and profit sharing in overlay Wi-Fi and LTE systems toward 5G networks, construction of enterprise 5G business ecosystem - case study of Huawei, and 5G new radio prototype implementation based on SDR. Section 3 focuses on different application scenarios of 5G, describing the prospect of 5G technology applied to distance medical education and clinical practice, research on the innovation path of logistics formats based on 5G technology, limiting energy consumption by decreasing packets retransmissions in 5G network, effective packet number for 5G IM WeChat application at early stage traffic classification, and software defined network (SDN) and OpenFlow protocol in 5G network. Section 4 focuses on topics from the 6G technology, describing the shift to 6G communications: vision and requirements, a semi-dynamic bidirectional clustering algorithm for downlink cell-free massive distributed antenna system, resource allocation for SWIPT systems with nonlinear energy harvesting model, and a resource allocation scheme with delay optimization considering mmWave wireless networks.

xxii

Section 1: Telecommunication, Antenna and Bandwidth aspects of 5G

CHAPTER 1

Mobile Communication Through 5G Technology (Challenges and Requirements)

Naseer Hwaidi Alkhazaali1,2, Raed Abduljabbar Aljiznawi1,2, Saba Qasim Jabbar1,3, Dheyaa Jasim Kadhim3 1 Signal School of Electronic Information and Communication Engineering, Huazhong University of Science and Technology, Wuhan, China. 2 Ministry of Communication, Iraqi Telecommunication and Post Company, Baghdad, Iraq. 3 Electrical Engineering Department, University of Baghdad, Baghdad, Iraq.

ABSTRACT Mobile communication through 5G technology is the key objective of this work. Existing research works in mobile communication through 5G

Citation: Alkhazaali, N., Aljiznawi, R., Jabbar, S.and Kadhim, D. (2017), Mobile Communication through 5G Technology (Challenges and Requirements). International Journal of Communications, Network and System Sciences,10, 202-207. doi: 10.4236/ ijcns.2017.105B020. Copyright      ! "#  $!censed under the Creative Commons Attribution International License (CC BY). http:// creativecommons.org/licenses/by/4.0

4

5G and 6G Communication Technologies

technology in world submitted a great necessary development towards 5G technology in different work approaches including hardware and software. 4G technology includes several standards under a common umbrella, similar to previous generations of communication technologies. Actually 4G is good for now, however if look at it in five or ten years, 4G will obviously not be able to meet requirements for new applications coming up in the next few years. With 5G will increase the data rate, reduce the end-to-end latency, and improve coverage. These properties are particularly important for many applications related to IoT and D2D, which they are recognized as ones of the technology components of the evolving 5G architecture. The major contribution of this paper is the key provisions of mobile communication through 5G (Fifth Generation) technology of which is seen as consumer oriented. In 5G technology and mobile consumer has given top priority over others. 5G technology is to make use of mobile phones within very high bandwidth. The consumer never experienced the utmost valued technology as 5G.The 5G technologies comprise all types of sophisticated features which make 5G technology most governing technology in the vicinity of future. Keywords: 5G Technology, WLAN, GSM, LTE, PLMN

INTRODUCTION Mobile and wireless networks have made significant improvement in the last few years. At the current time many mobile phones have also a WLAN adapter. One may expect that near soon many mobile phones will have Wax adapter too, besides their 3G, 2G, WLAN, Bluetooth etc. adapters. We are using IP for generations, 2.5G or 3G Public Land Mobile Networks (PLMN) on one side and WLAN on the other, developed study on their incorporation. With reference to the 4G, its focal point is towards flawless integration of cellular networks such as GSM and 3G. The multiple consumers put plants as it should be for 4G, but private security mechanisms and private support for the operating system in the wireless test techniques remain. However, the application of a combination of different wireless networks (such as PLMN and WLAN) is in practice until the present time. Although, different wireless networks from only terminal are used absolutely, there is no combining of dissimilar wireless access technologies for an equal session (e.g., FTP download). The predictable Open Wireless Architecture (OWA) in is targeted to offer open baseband processing modules with open interface parameters. The OWA is related to MAC/PHY layers of future (4G) mobiles

Mobile Communication through 5G Technology (Challenges and ...

5

[1]. New error-control schemes can be downloaded from the Internet and augmentation is seen towards the customer terminals as a focus on the 5G mobile networks. The 5G terminals will have software defined radios and modulation scheme and the 5G mobile terminals will have access to diverse wireless technologies at the same time. And also 5G mobile terminal should be proficient to merge special flows from different technologies. The 5G terminal will make the final selection among diverse mobile access network providers for a particular service. The network will be reliable for managing user-mobility. The paper gives the concept of intelligent Internet [2] phone where the mobile can prefer the finest connections [3].

CHALLENGES IN MIGRATION FROM 4G TO 5G Presently, 5G is not a term officially used for any particular specifications.3GPP standard release beyond 4G and LTE [4]. 5G Technology is a name used in a range of research papers and projects to point to the next most significant stage of mobile communication values beyond the 4G standards. The execution of standards under a 5G umbrella would likely be around the year of 2020. The following are the main constraints for migrating from 4G to 5G. A.

B.

; D.

Multi mode user terminals:- This trouble caused by means of 4G can be solved by using software radio approach. There will be an essential to design a single user terminal that can operate in different wireless networks and overcome the design troubles such as boundaries on the size of the device, its cost and power utilization. Choice among various wireless systems:- Every wireless system has its distinctive characteristics and roles. The choice of most !   !%   ' %  %!  at precise time will be applied by making the choice according      %!      * +*!   ' ) requirements.   !     !!       !          %    !  %' !  % %  scheme to the necessities of the technologies currently sharing the spectrum. See also the IEEE 802.22 standard for Wireless Regional Area Networks [8]. High altitude stratospheric platform station (HAPS) systems.

5G TECHNOLOGY REQUIREMENTS As a result of this blending of requirements, many of the industry initiatives that have progressed with work on 5G identify a set of eight requirements:  2. 3. 4. 5. 6. 7. 8. 



Mobile Communication through 5G Technology (Challenges and ...

13

extension after 4G; it also technically named “IMT-2020”, developed by the International Telecommunication Union (ITU). The digit 2020 means it is estimated to be widely published for commercial use in the year of 2020, and the 5G technology is now under experiments because it must fully satisfy standards of some key indicators: 1) user’s experience rate: 0.1 - 1 Gb/s; 2) low latency: the end-to-end delay is reduced to milliseconds; 3) connection density: one million/km2; 4) high data rate: the theoretical downlink rate of 5G network is 10 Gb/s. There are a large number of 5G required technologies, such as ultraintensive heterogeneous network, micro base station, beam forming, device   ‹!“  "  Manager (VIM) manages virtualized resources, fast installation images, and error reporting information.

Proofs of Concept (PoC) With the MEC framework and architecture, the standardization group of ETSI MEC Industry Enabling Group (IEG) is adopting MEC technologies by PoCs. These 13 PoCs [7] are now developed:             

Video user experience optimization via MEC; Edge video orchestration and video clip replay via MEC; Radio-aware video optimization in a fully virtualized network; Flexible IP-based services (FLIPS); Enterprise Services; Healthcare dynamic hospital user, IoT and alert status management; Multi-service MEC platform for advanced service delivery; Video analytics; MEC platform to enable low-latency Industrial IoT; Service Aware MEC Platform to enable Bandwidth Management of RAN; ;# =  ‹–— MEC enabled OTT business; MEC infotainment for smart roads and city hot spots.

MEC Services and Network Orchestration MEC Service Orchestration In order to promote the operation efficiency of MEC, the following servicerelated points can be taken into consideration: Resource allocation: Resources determine performance. VMs manage CPU, memory, storage, network bandwidth for tasks like computation ‰ƒ!!       – ? "’{     %!        achieve the speedup of networking in middleboxes. The experimental results show that for simple packet generation, the throughput on Linux platform increases from 6.46 GB/s to 9.68 GB/s for 1500B packets and from 0.42 GB/s to 5.73 GB/s for minimum-sized packets.

32

5G and 6G Communication Technologies

ClickOS Virtual Machine To realize flexibility, isolation, multi-tenancy and scalability, based on Xen, ClickOS virtual machine is built to provide a low-delay, high-throughput networking services. For a general view of ClickOS, it consists of three major parts: 1) Click modular route software, it makes it convenient to reuse the middlebox functionalities, and with more than 300 stocked elements, it also makes it easy to construct middleboxes; 2) MiniOS, it is a tiny operating system (built in Xen) that allows us to build efficient and virtualized middleboxes without unnecessary expenses; 3) Xen I/O subsystem optimization, which will allow a faster networking for traditional VMs. The ClickOS architecture is presented in Figure 17 [9]. Based on Xen, ClickOS is split into a Dom0 and DomUs where Dom0 is a privileged virtual machine acts as the driver domain and DomUs are the domains of guests or users comprising their virtual machine. For the drivers, originally, Xen has a split driver, where the back half runs in the driver domain and the front half in the guest VM. In the following part,  !! %           '   ! % !!  ClickOS. MiniOS running on it realizes all the basic functionalities needed in a virtual machine. Furthermore, each ClickOS VM consists of the Click modular router software running on top of MiniOS, and with the use of Xen store database, guest domains are able to share control information.

Figure 17. ClickOS architecture [9] .

Mobile Communication through 5G Technology (Challenges and ...

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Analysis of Xen network is implemented to test its bottlenecks. During the test, an Intel Xeon E3-1220 3.1 GHz 4-core CPU is used as a server, and a 16 GB memory along with an Intel x520-T2 dual Ethernet port 10 Gb/s card are used. Also, the server had Xen 4.2. The result is shown in Figure 18 [9] .To evaluate the performance in throughput rate with the use of the original driver model, experiments are conducted and the result shows in Figure 18 that the netfront driver yields poor throughput rate, especially for Rx, which barely handle 8 Kp/s, some modifications are put to the netfront driver to reuse the memory grants, and the Rx rate has increased to 344 Kp/s, though, still far from the 10 Gb/s line rate figure of 822 Kp/s. Next, the experimenter modified the software switch, replacing Open vSwitch with VALE (communicate using the netmap API), and the transmit rate has increased to 1.2 Mp/s for 64-byte packets. Thus, according to the experiments, ClickOS Network Input and Output is redesigned as Figure 19 [9] shows.There are three major steps in the re-design of I/O: 1) replace the standard but sub-optimal Open vSwitch backend switch with the high-speed VALE so that the NIC connects directly to the switch, and they increase the maximum number of ports on the switch from 64 to 256; 2) remove the netback driver from the pipe; 3) change the netfront driver to map the ring buffers into its memory spaces.

Figure 18. Throughput test [9] .

Figure 19. Standard Xen I/O pipe and ClickOS one [9] .

34

5G and 6G Communication Technologies

Also, there are some other changes like allowing asynchronous transmit to speed to transmit throughout, granted re-use, Linux support and Click modifications.

ClickOS Accomplishment In this part we introduce the accomplishment of ClickOS in 7 aspects: 1) ClickOS switch Performance; 2) boot time; 3) delay; 4) throughput; 5) state insertion; 6) chaining; 7) scaling out. The experimental settings are as follows: 1) A low-end Intel Xeon E31220 server with four 3.1 GHz core and 16 GB RAM which is used in most test; 2) A mid-range Intel Xeon E5-1650 server with three 3.2 GHz core and 16 GB RAM. As for the operating system, Linux 3.6.10 is used for dom0 and domU and Xen 4.2.0 in all situations, Xen 4.2.0 is used to generate packets and measure the rates. For the software, pkt-gen application is used to generate one 10 Gb/s Ethernet port.

ClickOS Switch Performance Experimenters have generated two 10 Gb/s Ethernet ports to test the scalability of the switch. As Figure 20 [9] shows, for the single port, the switch saturated the pipe for all packets sizes, from 64 bytes to 1024 bytes, and for the two ports, the switch as well saturated the pipes for all packets sizes while achieving 70% of line rates except for the 64 bytes packets.

Figure 20. Performance using one and two 10 Gb/s ports [9] .

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35

ClickOS VM Boot Time Figure 21 [9] shows that, for one ClickOS VM, it realized a time of 20.8 msecs, with 6.6 msecs to install a Click configuration, the total amount of time equates to approximately 28.8 msecs to the middlebox is up and running. Next, they measured how the booting number of ClickOS VMs can affect the boot time, and they booted large numbers of VMs (to 400) on the same system, and results show that it can up to a maximum of 2019 msecs. The increase in the boot time could be attributed to the contention on the Xen store and thus could be improved.

Delay Virtualization technologies are sometimes notorious for the introducing of extra layers, which may bring additional delay, so we can see how ClickOS’ network I/O pipe perform. When testing with disengaged ClickOS VMs, Figure 22 [9] presents a low delay of 45 μsecs and the number increased to 64 μsecs with 12 running VMs. Separately, Dom0 has a small delay of 41 μsecs. Consequently, compared with the unoptimized drivers of Xen and para-virtualized KVM drivers, ClickOS performs competitively in the measurement of delay.

Thoughput In this part the ClickOS is compared with the MiniOS, as we can see in the figure(c) of Figure 23 [9] , ClickOS’ transit performance is comparable to that of figure(a), which means that the ClickOS actually do not add much overhead. And the same is true for the receive performance except for the 64 bytes packets that drops from 12.0 Mp/s to 9.0 Mp/s.

State Insertion To test feasibility of ClickOS, it must allow quick reads and writes in order to enable middleboxes to be quickly configured. Using the method of python and cosmos, the result in Figure 24 [9] shows that for cosmos, the read time is about 9.4 msecs and write time is about 0.1 msecs. And for experimental comparison and completeness, the read time is 10.1 msecs and the write time is 0.3 msecs for python method.

36

5G and 6G Communication Technologies

Figure 21. ClickOS VM boot time [9] .

Figure 22. Idle VM ping delays for ClickOS, a Linux Xen VM, Dom0, and KVM using the e1000 or virtio drivers [9] .

Figure 23. Baseline throughput measurement [9] .

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37

Figure 24. ClickOS middlebox write and read [9] .

Chaining It is normal for middle boxes to be chained back to back, for example, the firewall can be followed by a flow monitor. It is necessary to test the throughput while the middleboxes are in a chain. Figure 25 [9] shows that a longer chaining leads to a lower rate: from 21.7 Gb/s with a chain of length 2 to 3.1 Gb/s with a chain of 9. The decrease in throughput rate can be attributed to the overload of single CPU because the experiment is implemented on one CPU. In addition, some extra copy operations and the load of Dom0 are to blame.

Scaling Out To test the scaling out ability, many VMs are launched on one CPU core and one 10 Gb/s port. The result in Figure 26 [9] shows that however, the number of the VMs are, a cumulative throughput rate equals to line rate for 512 bytes packets, 1024 bytes packets and 1472 bytes packets, and a rate of 4.85 Mp/s for 64 bytes packets. Then, when testing the scalability for additional CPU cores and 10 Gb/s ports. The result in Figure 27 [9] shows that up to 4 ports, the line rate for maximum-sized increased steadily, while invalid for 5 and 6 ports.

Middlebox Implementation on ClickOS Finally, to evaluate ClickOS performance under the context of real situation, different actual middleboxes are implemented on the ClickOS, and they are:

38

5G and 6G Communication Technologies

Wire (WR); Ether Mirror (EM); IP Router (IR); Firewall (FW); Carrier Grade NAT (CN); Software BRAS (BR); Intrusion Detection System (IDS); Load Balancer (LB); Flow Monitor (FM). For this implementation test, two of the low-end servers are used, one of which is used to generate packets to the other server. A total four CPU cores are involved in, and the ClickOS virtual machine is allotted with a single CPU core, and the other three are distributed to dom0. Under these settings, the throughput rate for each middlebox is shown in Figure 28 [9] . In general, ClickOS performs well enough for requirements of large packets, which saturated almost line rate for all middlebox  

Figure 25. Performance when chaining ClickOS VMs back-to-back [9] .

Figure 26. Running many VMs on one core and one 10 Gb/s port [9] .

Mobile Communication through 5G Technology (Challenges and ...

39

Figure 27. Cumulative throughput using multiple 10 Gb/s ports and one VM per port [9] .

Although for requirements of smaller packets, the rates drop from line rate, ClickOS is capable of processing the packets in millions/sec. To sum up, as one of 5G techniques, the network function virtualization addresses the problems brought by hard-ware based middleboxes effectively. And the virtual machine ClickOS provides an ideal platform for middlebox %!    % !“ %“  ! Œ> low delay, excellent state insertion, chaining and scaling out performance and validated high throughput rates for packet transmitting and receiving. Absolutely, the more research is being conducted, the better 5G network will be.

Figure 28. Performance for different middleboxes [9] .

40

5G and 6G Communication Technologies

CONCLUSIONS 5G changes mobile networking by MEC, NFV, edges and middleboxes. Not only can fog computing platforms, computation offloading, widespread computing, network slicing in MEC and middleboxes and polished ClickOs in NFV enhance QoE, but other promising technologies such as D2D communication will also advance networks around us. D2D technology greatly improves the speed and efficiency of communication. The use of D2D-based two-tier cellular networks also meets different communication situations. From the increase of transmission rate, the reduction of transmission  ! %'  % !“   > "   of Everything, multi-network convergence of heterogeneous networks, the    `^ !!    "! !  the more we research, the better 5G networks will be. Our future work includes: 1) solving the D2D peak communication latency issue when large amounts of devices use D2D communication simultaneously; 2) upgrading traditional cellular network by realizing the compatibility of D2D and cellular networks; 3) exploring other technologies, for example, millimeter waves, massive MIMO, NOMA (non-orthogonal !%!   ‚— ˜‚   \$    !  %! =;   `‚                    $™ % “    $ ŠX‹™ % “    the structure of the network element device and their application on the 5G; 6) introducing a 5G network cloudization frame based on SDN and NFV that    `^  $  ‰ Œ!> !!  >  openness; 7) analyzing the technical advantages and creative applications of „Š  ŠX‹> %  !!           !  network resource and standardization of hardware equipment.

     The authors declare no conflicts of interest regarding the publication of this paper.

Mobile Communication through 5G Technology (Challenges and ...

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REFERENCES 1. 2. 3.

4. 5.

6. 7. 8.

9.

White Paper (2016) Cisco Visual Networking Index: Global Mobile „# X š% >` % %  is PinPin and radiated power is PradPrad . then, (1) Figure 9 shows the accepted and radiated power for the proposed antenna. From the analysis of Figure 9, at 2.4 GHz, 7.8 GHz and 33.5 GHz the accepted power and the radiated power is high. For that reason, radiation   !    \  ƒ˜^¡“>…   % % Ÿ}  ' % Ÿ}X  \+‚>      is about 24.6% at 2.4 GHz for Wi-Fi. ƒ ^¡“>…=ƒ–>  % % Ÿ}  '  % Ÿ }X \+‚>      about 35.8% at 7.8 GHz for WiMAX. At 33.5 GHz, for 5G application, the

52

5G and 6G Communication Technologies

 % % Ÿ}  ' % Ÿ}X  \+‚>     ˜¢¥`^¡“ 5G. These data are discussed in Table 4.

Radiation 5G At 33 GHz, for 5G application, from the analysis of far-field in Figure 10, it is highly directive antenna and directivity is 8.4 dBi. Main lobe direction is 5.0 degree, angular bandwidth at 3 dB point is 62 degree and side lobe level Ÿ˜}

Figure 8;%      !   %posed antenna with respect to frequency.

Figure 9. Radiated and accepted power of the proposed antenna for different communication systems with respect to frequency.

Design of a Multiband Patch Antenna for 5G Communication Systems

53

The e-field for main lobe magnitude is 19.8 dBV/m, h-field for main lobe  Ÿ}ƒ’%  % Ÿ`‡}…’2. The gain of the radiation pattern is 5.06 for main lobe magnitude. All of them are discussed in Table 5.

WiMAX At 7.8 GHz, for WiMAX application, from the analysis of far-field in Figure 11, it is highly directive antenna and directivity is 6.62 dBi. Main lobe direction is 35.0 degree, angular bandwidth at 3 dB point is 49 degree and   !  ! ' !  Ÿ˜ } #    another good solution to compensate for the high propagation loss related to mmw bands is to use high gain antenna arrays for 5G mobile network comprising a narrower radiation beam [21]. Recent studies have reported  ' !   !          `^  !  %%!          ! %  % !    and minimize weather attenuations [22] [23] [24] [25]. In this article, an optimized design of a 10 × 1 linear 5G wideband frequency  ! $ %   ^# …  ^> – + ‚ ƒ X \  !+‡‚  „ ' !%   !  Antennas for Current and Future Wireless Communication Systems. Electronics Letters, 8, 1-17. https://doi.org/10.3390/electronics8020128 Zainarry, S.N.M., Nguyen-Trong, N. and Fumeaux, C. (2018) A X \    ƒ+‡‚"  X \ §p=KM,and Lp=KM+1. and, where K is a positive integer called as overlapping factor and it is chosen to be 3 or higher. ‚ #  %%  !              ! ! ­     ! !    % ! ' !%% with each other in the frequency domain. The amount of sub channels is twice the up sampling and down %! Œ' %!  ! $%% signals are complex-valued. Hence, if input and output signals are purely real/imaginary-valued then the existing TMUX is comparable to a critically sampled TMUX. This happens because the sample rate (counted in terms of real-valued samples) of the SFB output and AFB input is equal to the sum of  %!     !!ƒ Œ !“Ÿ„“Ÿ„>   „ %  !   %%  ! +§p©‘=¯Ÿ„‚>   included either in AFB or SFB output [9] .

OQAM Pre/Post Processing The TMUX system transmits OQAM symbols instead of QAM symbols. The pre-processing block, which utilizes the transformation between QAM and OQAM symbols, is shown in Figure 4. As can be seen, the first operation is a simple complex-to-real conversion, where the real and imaginary parts of a complex-valued symbol ck,l are separated to form two new symbols dk,2l and dk,2l+1 (this operation can also be called as staggering). So the order of these original symbols depends upon the sub channel number, i.e., the conversion is distinct for even and odd numbered sub channels. The complex-to-real conversion upsurges the sample rate by a factor of 2. After    %  !%!±k,n sequence [9] . A possible choice is (1)

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5G and 6G Communication Technologies

However, it should be noted that the signs of the ±k,n sequence can be chosen arbitrarily, but the pattern of real and imaginary samples has to follow the above definition. For example, an alternative sequence

(2) The input signals are purely real or imaginary-valued after the OQAM pre-processing. The post-processing block is shown in Figure 5 and again there are two slightly different structures depending on the sub channel number. The

%  !%! sequence that is followed by the operation of taking the real part.

Figure 4. OQAM preprocessing [9] .

Figure 5. OQAM post processing [9] .

FBMC vs OFDM Waveform Contenders for 5G Wireless ...

85

The second operation is real-to complex conversion, in which two successive real-valued symbols (with one multiplied by j) form a complex-valued symbol (this operation is also called as de-staggering). The real-tocomplex conversion decreases the sample rate by a factor 2. As can be seen, the first operation is a simple complex-to-real conversion, where the real and imaginary parts of a complex-valued symbol ck,l are separated to form two new symbols dk,l and dk,2l+1 (this operation can also be called as staggering). The order of these new symbols depends on the sub channel number, i.e., the conversion is different for even and odd numbered sub channels. The complex-to-real conversion increases the sample rate by a factor of 2. The  %  !%! \  ±k,n. [9] .

Synthesis and Analysis Filter Banks ƒ X}=%%! =  ! ƒ shown in Figure 3, the input signals ( ) X z k , where k M = ԫԫԫŸ>>> >>  %%! =’  !      ! ^“ k ( ) The SFB output signal Y(z) is formed when all sub signals are added   ƒ ƒX} =! ! =  %!   X # %!«+“‚  !   ! ! X“$+‚  !   %!  of M/2 to form output signals ( ). X z k In the case of chosen class of com%! Œ!  ! $>!!  ! !       !   !²•#%!!>   %   !$      > !> ' "  # +"#‚>  „  Š  $ (SDN), Big Data Analytics, Cloud Computing Share and Cite:

INTRODUCTION The roles of SDN, Cloud, IoT, and Big Data in 5G Networks have raised great interest recently [1] . According to the IEEE Computer Society (IEEE CS 2022 Report [2] ), Cloud Computing (Cloud), Big Data Analytics (Big Data), Internet of Things (IoT), and Software Defined Networks (SDN) are among 4 of 20+ emerging technologies as illustrated in Table 1. In the meantime, based on the report in IEEE Communications Society (IEEE ComSoc Technology News [3] ), among top 10 trends in 2015 as listed in Table 2, 5G, Virtualization (SDN & NFV), everywhere connectivity for IoT & IoE, and Big Data are also included. Combining both reports, we can identify 5G, Cloud, IoT/IoE, Big Data, and SDN as the five most worthwhile ICTs (information & communications technologies) to watch out up to 2020 in term of their potential, convergence and applications. Table 1. IEEE computer society 2022 report.

Technology & Indexing Terms 1. Security Cross-Cutting Issues

9. Multicore

17. 3D Printing

2. Open Intellectual Property Movement

10. Photonics

18. Big Data and Analytics

3. Sustainability

11. Networking and Interconnectivity

19. Machine Learning and Intelligent Systems

Mobile Communication through 5G Technology (Challenges and ...

4. Massively Online Open Courses

12. Software De  Š  $

20. Life Sciences

5. Quantum Computing

13. High Performance Computing

21. Computational Biology and Bioinformatics

6. Device and Nanotechnology

14. Cloud Computing

22. Robotics

7. 3D Integrated Circuits

15. Internet of Things

8. Universal Memory

16. Natural User Interfaces

95

Table 2. IEEE communications society top 10 trends 2015.

1) 5G 2) FIBER EVERYWHERE 3) VIRTUALIZATION, SDN & NFV 4) EVERYWHERE CONNECTIVITY FOR IoT & IoE 5) BIG DATA, COGNITIVE NETWORKS 6) CYBERSECURITY 7) GREEN COMMUNICATIONS 8) SMARTER SMARTPHONES, CONNECTED SENSORS 9) NETWORK NEUTRALITY, INTERNET GOVERNANCE 10) MOLECULAR COMMUNICATIONS X  % ' !%%   ' ­";# "! illustrates how the infrastructure deployment of 5G mobile broadband and the architectural integration of Cloud Computing strongly impact the development of IoT, Big Data, and SDN. This paper explores the technical relationships     '        ' !  % and applications currently under development at NCTU based on these technologies.

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5G and 6G Communication Technologies

THE ROLES OF 5G, IOT, BIG DATA, CLOUD, AND SDN TILL 2020 Although so far the 5G mobile broadband requirements and standard specifications are not ready yet, 5G technology research & development are already started and some 5G features or subsystems are readily available. By Year 2020 the commercial 5G will be available and IoT applications will be deployed everywhere with mobile broadband technology. Moreover, the Big Data generated by IoT applications will become a norm and Cloud will be largely utilized to compute, store and virtualize network functions (NFV). Also, the underlying network infrastructure will adopt SDN to reduce both capital expense (CAPEX) and operational expense (OPEX). Figure 2 further illustrates the roles of 5G, IoT, Big Data, Cloud and SDN and their relationships. This figure is modified based on the reference [4] by inserting 5G mobile broadband in the center.

TECHNICAL RELATIONSHIPS AMONG IOT, BIG DATA, CLOUD, & SDN IN 5G ERA Based on Figure 2, we develop Figure 3 to better explain the technical relationships among IoT, Big Data, Cloud, and SDN in the 5G mobile broadband services (5G MBS) [5] . First, IoT is capable of generating Big Data with four Vs: volume, velocity, variety and veracity. Then, Cloud is brought in for Big Data storage and processing. Finally, SDN is employed to provide more efficient and flexible networks for inter-Cloud data transport. Out of Big Data, Cloud, and SDN, advanced technologies such as machine learning analytics, Cloud RAN and softwarized 5G then are developed.

Figure 1. ICT major trends for 2015-2020.

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Figure 2. Relationship among 5G, IoT, Big Data, Cloud and SDN based on [4] .

As illustrated in Figure 4, 5G will serve as a better gateway and transport network for IoT applications so that IoT data can be delivered     ! !!">"# !!     major sources of Big Data by producing large volume, fast velocity, and many varieties of data [6] as illustrated in Figure 5. Finally, Figure 6 shows that Cloud can be adopted in the 5G Radio Access Network (RAN) and turns it to a Cloud-based RAN (C-RAN). Both SDN and NFV have been applied to data center in the cloud to enable better load balance and resource allocation of the cloud. SDN has also been applied to 5G mobile broadband   $ ! >    management and improve network resource utilization.

Figure 3. Technical relationships among 5G & IoT, Big Data, Cloud, and SDN.

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5G and 6G Communication Technologies

Figure 4. 4G/5G as the gateway for IoT applications.

Figure 5. Relationships between IoT and Big Data.

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Figure 6. C-RAN architecture in 5G mobile network.

ONGOING PROGRAMS AND APPLICATIONS AT NATIONAL CHIAO TUNG UNIVERSITY (NCTU) In this section, we describe five ongoing programs and applications at NCTU that are closely related to the SDN, Cloud, Big Data, IoT, and 5G technologies [7] -[17] .

Program on SDN-Enabled Cloud-Based Broadband and Wireless Network Technologies and Services This program is a national program to support the SDN Industry-Academia Cooperation, led by both NCTU and CHT (Chunghwa Telecom, the largest mobile & telecom operator in Taiwan) with 5 other networking and communications companies in Taiwan. This is a multi-year program with the target to set up a testbed for end- to-end testing and to help establish an ecosystem for local SDN industry. Figure 7 shows the scope of this program that covers mobile access, WiFi access, broadband core and data center cloud end-to-end application systems. Figure 8 further shows both the structure of the program and its network    Š;#š> ;¡#    '  Š! # Hwa University (NTHU).The program includes research and development involving 4G/LTE, B4G/5G, SDN, Cloud, SDN for Wi-Fi, and SDN for …ƒŠ  ! #    '   %!  multitenant network automation can be developed and deployed.

Program on Big Data Analytics " #  $ agement Data In this program, we address the network performance issues with two experimental networks: BML at NCTU campus and ITRINET of ITRI. The architecture of the BML experimental network is illustrated in the lower part of Figure 9 that consists of a 4G RAN, a 4G Core and a Cloud environment. For the 4G RAN, both indoor and outdoor environment are taken into consideration. The left upper part of Figure 9 shows the scope of the ITRINET experimental network that covers a much greater area of Hsinchu County than NCTU and includes additionally ITRI (Industrial Technologies Research Institute), HSIP (Hsinchu Science Industrial Park) and THR (Taiwan High-speed Rail, Hsinchu station). The right upper part of Figure 9 illustrates how ITRINET covers various R&D buildings in the corporate campus of ITRI such as B11, B12, B51, etc.

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Figure 7. Applications of SDN & cloud in 4G/LTE & 5G wireless networks.

Figure 8. SDN technical R&D center and projects at NCTU.

Figure 10 illustrates how Big Data analytics based on InfoSphere or Spark is performed on ITRINET for the purpose of network optimization. X>   $       and network management data are collected.

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5G and 6G Communication Technologies

Figure 9. Greater Hsinchu 4G/LTE & Future 5G experimental network ITRINET (include ITRI, NCTU, THR & HSIP).

Then, data analytics methods based on machine learning, data mining and statistical modeling are applied to analyze the collected data. Finally, we apply the results thus generated to network performance evaluation and %“%' $!%  < #  whole operational cycle includes the technologies of 4G/LTE, B4G/5G, Big Data Analytics>;!>#  .

Application on IoT Platform Integrated with Data Generation and Data Analytics This is an application where we set up an IoT platform integrated with data generator and data analytics capabilities as illustrated in Figure 11. A common challenge for IoT/M2M service providers is how to test their large scale IoT/M2M applications with the near realistic data that the system will handle in a production environment. As such tests may involve not only a large number but also a large variety of sensors, deploying a

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testing environment that contains all the necessary sensors turns out to be an infeasible, if not impossible job. To tackle this problem, we develop a data generation method (illustrated in the lower part of Figure 11) based on streams generation capabilities of IBM InfoSphere and Spark to emulate data from a large number and a large variety of sensors. Such generated data will be sent into and processed by the applications residing on the IoT/M2M platform (illustrated in the middle part of Figure 11).

Figure 10}„!#   "#"Š#

Figure 11. IoT platform integrated with data generation and data analytics.

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5G and 6G Communication Technologies

Finally, the data sets produced by the IoT/M2M applications are analyzed by data analytics engines in IBM InfoSphere and Spark (illustrated in the upper %X ‚#   '      !  '  % %!    !  ' …  methodology, we are able to test our factory management IoT/M2M application     !  !! %!!  ! '  of sensors. This application development uses both IoT and Big Data Analytics.

   %   # ' *#ing & Measurement The automatic Testing & Measurement (T&M) of DUT (Device under Test, including 4G/LTE SD, UE/CPE) for 4-Stage (namely, conformance, interoperability, operator-IOT and field trials) testing is shown in Figure 12. …  %%!    ! !    \     !     „š# %          {    %     > the parameters for feature extraction can be measured by the testing and     \%  ƒ Œ%!   „š# !      in Table 3. In this example, the DUT type relational table is a 4 × 4 table with ˜ !‚„š#„ ' #% >‚š%!"+X^"‚>‚„Š (TTL), and 4) HTTP (User Agent). This device relational table is created by %%!!' !!   $ ‰ §# š  ! #  Œ%    >  are able to identify the types of DUT such as Dongle, Smartphone, CPE, etc.

Figure 12. Data generation by endpoints from testing & measurement.

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Table 3. DUT relational table. DUT Device Type

UE FGI

DNS-TTL

HTTP-User Agent

Dongle A

0100 0110 0000 0101 0001 1000 0000 0000

128

Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/43.0.2357.134 Safari/537.36\r\n

Dongle B

0101 1111 0000 1111 1111 1100 1000 0000

63

Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/43.0.2357.134 Safari/537.36\r\n

Smart Phone

0101 1110 0000 1101 1101 1000 1000 0000

43

iPhone6,2/8.3 (12F70)\r\n

CPE

0101 1111 0000 1111 1111 1100 1000 0000

127

Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/43.0.2357.134 Safari/537.36\r\n

ƒ  Œ%!   %%!  !  #¶=     evaluate LTE QoS. When evaluating LTE QoS, it is required to setup testing      *! ' !# X! # %! +#X#‚ (as shown #! ˜‚   ! %$  %   >   default bearer or dedicated bearer, in LTE network. Each bearer has its own QoS level. We can establish a dedicated bearer of guarantee bit rate (GBR) for applications such as VoIP, or a default bearer of basic QoS level for %%!!$  !   … #X#  ! %$  %    to packets’ IP address, port number, protocol, direction. The information,

 ' >       !  !>X}  browsing, and Line chat. We are unable to give different QoS levels to these applications. To tackle this problem, we propose a new architecture which integrates deep packet inspection (DPI) with TFT to provide a higher

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5G and 6G Communication Technologies

!*! ' !%%!…   ‰       %%!% >#X# !!  % !  %$  ! …  %'   >  ‰   !'   through the bearer of suitable QoS level.

   %   # + * APP’s of SD To fulfill the Quality of Service (QoS) requirements from users, it is important to make effective use of the network resource. We can optimize the performance of a network by applying data analytics to traffic engineering. In particular, it is important to classify mobile applications traffic intended by the user with data analytics. We propose a HMM-based (Hidden Markov Model) model to classify the mobile applications. By surveying related work, we have realized that there are different handshake patterns of well-known application protocols. ƒ!>      '    %   !  %%!> we discover that every mobile Internet service has its unique negotiation process at the beginning when service starts. HMM was widely used to  “   such as speech recognition, handwriting recognition. In our method, we extract the packet size sequence and packet     \        %$      ¡== model. Figure 13 shows our designed model structure of HMM. Xn is the hidden variable. It represents the transmission states which cannot be observed directly. Because of the unknown transmission states, we need to use observation variable as training features to build the mobile application models. ¡  >·¸  \   %$  ‰  is the observation symbol of packets transmission direction. is the observation of packet size that quantizes to a certain range of packet size. We quantize smallest packet size to number 1 and largest packet size to number 8. The rest of packet sizes are then divided into six groups.

DRB

DRB2

DRB2

DRB3

DRB3

DRB3

DRB3

DRB3

DRB1 (default bearer)

UL Packet Filter ID

1

2

3

4

5

6

7

8

255

6

1

5

3

7

2

6

Packet Filter Evaluation Precedence

6 (TCP)

-

-

50 IPSec (ESP)

17 (UDP)

6 (TCP)

17 (UDP)

6 (TCP)

Protocol Number (IPv4)/ Next Header (IPv6)

-

-

-

-

-

IPv4: 172.168.8.0 [255.255.255.0] IPv6: 2001:0ba0:: [ffff:ffff::]

-

IPv4: 172.168.8.0 [255.255.255.0] IPv6: 2001:0ba0:: [ffff:ffff::]

Remote Address and Subnet Mask

-

-

-

-

-

-

-

60051

Single Local Port (UE)

-

-

-

-

-

60100:60200

-

-

Local Port Range (UE)

-

-

-

-

-

-

60201

-

Single Remote Port Range (NW)

Table 4# X! # %! +#X#‚š %  %$  !  

-

-

-

-

60300: 60400

-

-

-

Remote Port Range (NW)

-

-

-

0x0F80F 0000

-

-

-

-

IPSec SPI Range

-

-

00101000, Mask = 11111100

-

-

-

-

-

-

-

-

-

-

-

-

-

Type of Ser- Flow vice (IPv4)/ Label # ;! (IPv6) (IPv6) and Mask

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Figure 14 shows the main process of our classification system. First, we process the collected packets by reading from original PCAP files, and extract the necessary field of packet header, including source IP, destination IP, source port number, destination port number, packet timestamp, and packet size. Second, we use packet header information (5-tuple: source IP, destination IP, source port number, destination port number, and protocol) to process packets with the same 5-tuple information into a unit of mobile application traffic flow, and extract the features of each application flow.

Figure 13;!  !"  %%! 

Figure 14# % %% !  

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109

Third, in learning process, we use the extracted features which are quantized into corresponding symbol sequence to training the HMM-based application models for different applications. Finally, we can identify the new traffic flow by finding out the maximum value of log likelihoods derived from different application models.

CONCLUSION This paper presents a forward looking view of the convergence of IoT, big data, cloud, SDN technologies along with the arrival of 5G mobile broadband networks. We intend to demonstrate the technical relationships of those technologies and the compelling programs and applications that can be created when two, three or more of those technologies converge. Due to the nature of fast evolution of ICT and the ongoing innovation of those five technologies, this paper should be updated on annual basis to keep the related information up to day with the ICT major trends.

ACKNOWLEDGEMENTS This work was supported by the Ministry of Science and Technology (MOST) of Taiwan and National Chiao Tung University under grants: MOST 1032622-E-009-012-, 103-2221-E-009-138-, 103-2218-E-009-032-, 104-2221E-009-023- and NCTU-ICTL 104Q707.

     The authors declare no conflicts of interest.

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REFERENCES 1.

Lin, B-S.P. (2015) The Roles of SDN, Cloud, IoT, & Big Data, in 5G Networks and Their Technical Relationships & Applications. Invited Talk, IEEE VTS APWCS 2015, Singapore. 2. Alkhatib, H., et al. (2015) IEEE CS 2022 Report. IEEE Computer Society, IEEE. 3. Neira, E.M. (2015) IEEE ComSoc CTN Special Issue on Ten Trends That Tell Where Communication Technologies are Headed in 2015. 4. Tucker, L. (2013) On the Cloud of the Future. Cloud Computing Summit, San Jose. 5. Kleiner, T. (2015) 5G Research in Horizon 2020. 6. Thadani, A. and Andreoli, P. (2014) Realizing Business Value from Convergence of IoT + Big Data Technologies. 2014 Midwest Architecture Community Collaboration (MACC) Conference, Minneapolis, 13 November 2014. 7. Dinh, H.T., Lee, C., Niyato, D. and Wang, P. (2013) A Survey of Mobile Cloud Computing: Architecture, Applications, and Approaches. Wireless Communications and Mobile Computing, 13, 1587-1611. http://dx.doi.org/10.1002/wcm.1203 8. Lin, B-S.P., et al. (2013) The Design of Cloud-based 4G/LTE for Mobile Augmented Reality with Smart Mobile Device. Proceedings of 2013 IEEE International Symposium on Mobile Cloud, Computing, and Service Engineering (IEEE Mobile Cloud 2013), Redwood City, 25-28 March 2013, 561-566. http://dx.doi.org/10.1109/sose.2013.57 9. Lin, B.-S.P., Tung, L-P., Hsieh, I-C., Liu, T-H. and Chou, S-Y. (2015) The Design of Big Data Analytics for Testing & Measurement and #  X!    Œ%  ! ˜^’§# Š  $    IEEE WOCC 2015, Taipei, 23-24 October 2015, 40-44. http://dx.doi. org/10.1109/wocc.2015.7346173 10. Lin, B-S.P., Tung, L-P., Hsieh, I-C. and Chou, S-Y. (2015) Performance Estimation of MAR for Outdoor Navigation Applications on an 5G Mobile Broadband by Mobile Smart Devices. Proceedings of IEEE APWCS 2015, Singapore, 19-20 August 2015, 1-6. 11. Lin, F.J., Ren, Y. and Cerritos, E. (2013) A Feasibility Study on Developing IoT/M2M Applications over ETSI M2M Architecture.

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12.

13.

14.

15.

16.

17.

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Proceedings of the 1st International Workshop on Internet of Things Technologies (IoTT), Seoul, 15-18 December 2013, 558-563. http:// dx.doi.org/10.1109/icpads.2013.100 Chen, H.W. and Lin, F.J. (2014) Converging MQTT Resources in ETSI Standards Based M2M Platform. Proceedings of IEEE iThings, Taipei, 1-3 September 2014, 292-295. http://dx.doi.org/10.1109/ ithings.2014.52 Cerritos, E. and Lin, F.J. (2014) M2M-Enabled Real-Time Trip Planner. Proceedings of the 2nd International Workshop on Internet of Things Technologies (IoTT), Hsinchu, 16-19 December 2014, 886-891. http:// dx.doi.org/10.1109/padsw.2014.7097902 Lusiarta Putera, C.A. and Lin, F.J. (2015) Incorporating OMA Lightweight M2M Protocol in IoT/M2M Standard Architecture. Proceedings of IEEE World Forum on IoT, Milan, 14-16 December 2015, 559-564. http://dx.doi.org/10.1109/WF-IoT.2015.7389115 Adrianto, D. and Lin, F.J. (2015) Analysis of Security Protocols and Corresponding Cipher Suites in ETSI M2M Standards. Proceedings of IEEE World Forum on IoT, Milan, 14-16 December 2015, 777-782. http://dx.doi.org/10.1109/wf-iot.2015.7389152 Lin, F.J. and Chen, H. (2015) Improving Utilization and Customer Satisfaction of Parking Space with M2M Communications. Proceedings of IEEE World Forum on IoT, Milan, 14-16 December 2015, 465-470. Lin, F.J., Tsai, W., Cerritos, E., Lin, B.-T. and Hu, W.-H. +`‚ "    ƒ!  ;  X  == Communications. Proceedings of IEEE World Forum on IoT, Milan, 14-16 December 2015, 160-165. http://dx.doi.org/10.1109/wfiot.2015.7389045

CHAPTER 7

Planning and Profit Sharing in Overlay WiFi and LTE Systems toward 5G Networks

Danielle Saliba1, Rodrigue Imad2, Sebastien Houcke3, Bachar El Hassan4 1 Signal and Communication, Mines Telecom Atlantique, Brest, France. 2 Mechatronics, University of Balamand, Al Kurah, Lebanon. 3 Signal and Communication, Mines Telecom Atlantique, Brest, France. 4

Telecommunication and Networking, Lebanese University, Tripoli, Lebanon.

ABSTRACT With the increasing demand for data traffic and with the massive foreseen deployment of the Internet of Things (IoT), higher data rates and capacity are required in mobile networks. While Heterogeneous Networks (HetNets) are under study toward 5G technology, Wireless Fidelity (WiFi) Access Points

Citation: Saliba, D. , Imad, R. , Houcke, S. and El Hassan, B. (2019), Planning and   {' !…X§#  `^Š  $Z!ware Engineering and Applications, 12, 491-508. doi: 10.4236/jsea.2019.1211030.. Copyright‡      ! "#  $!censed under the Creative Commons Attribution International License (CC BY). http:// creativecommons.org/licenses/by/4.0

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(APs) are considered a potential layer within those multiple Radio Access Technologies (RATs). For this purpose, we have proposed in this paper a novel WiFi dimensioning method, to offload data traffic from Long Term Evolution (LTE) to WiFi, by transferring the LTE energy consuming heavy users, to the WiFi network. First, we have calculated the remaining available capacity of the WiFi network based on the estimated load of each WiFi physical channel using the overlapping characteristic of the channels. Then, we were able through this dimensioning method, to calculate the minimum needed number of WiFi APs that ensure the same or better throughput for the LTE transferred users. By this method, we have ensured additional capacity in the LTE network with minimum investment cost in the WiFi network. Finally, we have estimated the profit sharing between LTE and WiFi by considering data bundles subscription revenues and the infrastructure capital and operational costs. We have calculated for each network the profit share using a coalition game theory Shapley value that pinpoints the benefit of the cooperation using the proposed dimensioning method. Keywords: §# …X {‰  ; Œ  > `^> ¡     Š  $> %! ‹!   

INTRODUCTION With the increasing demand for wireless communication technologies and data traffic, the main limitation in mobile networks is the lack of available licensed spectrum. Operators have limited and expensive spectrum, so they need to plan the effective utilization of their radio resources. This can be done by offloading mobile data between licensed and unlicensed spectrum [1]. Multi Radio Access Technology (RAT) solution, as the integration between Long Term Evolution (LTE) and Wireless Fidelity (WiFi), is an alleviating solution to ensure additional capacity and distribute the       !   ¡    Š  $+¡ Š ‚. WiFi is a potential candidate to support LTE in HetNets for many reasons:   '       ¥!     !  !      Œ  % ”•— …X ƒ  Points (APs) are easily and quickly deployed in many residential areas and indoor environments, with affordable cost of investment and without any restrictions in hardware size or physical customization; and, most of the smart devices are equipped with WiFi capabilities. Finally, in contrast with the licensed spectrum used in LTE cellular networks, unlicensed spectrum

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of WiFi systems is less expensive, where 802.11x WiFi network may have better throughput and consume less power than the cellular network [3]. However, most of current WiFi networks consist of randomly deployed WiFi cells since there are no limitations or policies on WiFi AP deployment [4]. The unplanned installation of APs may cause the WiFi networks to be %!      ! There have been several studies on WiFi APs deployment problems. In [4], the minimum required number of WiFi APs was investigated based on the active users’ density, the coverage of the WiFi AP and the transmission probability of a user, without taking into consideration the WiFi network available capacity. In [5], the authors propose WiFi deployment algorithms based on realistic mobility characteristics of users to deploy WiFi APs for continuous service for mobile users, based on maximum continuous coverage where WiFi network capacity was not considered. In [6], the  ƒ \ …X‰    \! '   !'  \ —  ' > ­%'  !  ‰!   !!  perform any mathematical analysis for this problem. " >    '  !“    %   ‰ between LTE and WiFi based on different criteria and assumptions. In [1], authors proposed a Low Amplitude Stream Injection (LASI) method to enable the simultaneous transmissions of WiFi and LTE frames in the same   ! '   ‰"”•> ‰…X was analyzed based on the Remaining Throughput Scheme (RTS) for WiX  !  " ”•>   ‰ …X   $  %%        „   Š  $ +„Š‚       …X „ '    ‰     random or based on the probability of WiFi channels occupation or on the Channel State Information (CSI) either. Instead, it is based on the exact information sent by the WiFi network informing the LTE eNB about its remaining average capacity. This remaining average capacity depends on the estimated channels load of the physical layer of the WiFi network [9]. In this paper, we consider the average of the channels load or occupation value of the channels that has been calculated in [9], however this value has been averaged for  ' ! % $   …X  $}   averaged value, we have a global estimation calculated through the multiple APs to be collected on a higher control node of the network to estimate the remaining available capacity and to facilitate the measurements collection and processing time. Therefore, our framework is divided into two phases to transfer cellular  §#}…X 



#  %       '   !!     %      !   ‰     §# system. The second phase is WiFi APs dimensioning. This is considered through WiFi APs remaining capacity calculation, and it is based on the remaining throughput of each WiFi AP based on the average occupation or load value of the physical channels.

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PROBLEM FORMULATION The WiFi network should assure a minimum acceptable and predefined average per user throughput for an efficient LTE offloading. Based on this average per user throughput, we will calculate the minimum required number of WiFi APs in the overlay network. As illustrated in Figure 1, we consider a scenario with one LTE BS and K WiFi APs operating separately in licensed and unlicensed spectrum, respectively. In our scenario, we assume a coverage area of 802.11n WiFi APs with no interference, each transmitting on an orthogonal channel in the 2.5 GHz unlicensed spectrum, selected based on the minimal calculated load value of the channels referring to the algorithm in [9]. This model has also been adopted in other literatures, such as [4] and [7]. Following the same principle, the analysis of 5 GHz spectrum and 802.11ac could be applied [9]. The coexistence of WiFi and LTE could be facilitated by assuming that an inter-system coordinator exists, which performs the WiFi user transfer and resource allocation, as in [7]. To note that our proposed system is very useful for the case where LTE-A and WiFi are deployed by the same network operator, in this case, the inter-system coordinator can be implemented by the cellular network operator itself. Otherwise, it can be implemented by a third-party vendor that provides service enhancement for both WiFi and LTE. In our paper, the basics of the problem formulation for the LTE eNB are an energy minimization problem and not a throughput maximization problem. The energy minimization solution consists in identifying the users who consume the highest energy and require high throughput rates which are considered in our simulation greater or equal to 20 Mbps [10]. This       %    …X   $>    ‰    should be in range with an AP having an adequate capacity. "      '  §#  ! ‰  to WiFi network, the operator needs to determine the resource allocation policy, in terms of Resource Blocks (RBs) assignment and transmission power [8]. We consider the downlink operation of one LTE-A macro cellular BS for a time period of T subframes, possibly expanding over multiple frames. There exists a set of Nc users within the cell.

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The BS has a set of M available RBs that can be allocated to users in each subframe (t=1,2,Օ,T)(t=1,2,Օ,T). The value of M depends on the available spectrum. Hence, there are in total (MѽT)(MѽT) RBs. The system is considered quasi-static, i.e., users do not join or leave the cell during the current time period, and channels do not change significantly (flat fading). Note that, even if channels change rapidly, the eNB will not be aware of this fact, as users transmit their Channel Quality feedback Information (CQI) parameters only once during this time period. In the beginning of the period, the eNB devises the RB assignment and power allocation policy for serving his users. Let xnm(t)Ѯ{0,1} denote whether RB mѮMis allocated to user nѮNc during subframe t. Let Pnm(t) denote the respective transmission power. For each RB, the BS can determine a different transmission power. However, the total power consumption should not exceed a maximum level of aggregated transmission power Pmax (Watt). Assuming orthogonal allocation of RBs, and ignoring inter-cell interference, i.e., we assume that proper Enhanced Inter-Cell Interference Coordination (eICIC) techniques are applied, the instant rate for each user n is calculated by [8]:

(1) where Wb is the symbol rate per RB, hnm the channel gain of user n in }   % >¼2 is a parameter considering the variance of the noise [11]. These parameters are estimated through the CQI feedback that is provided by the users, once every period T. Based on this policy, the operator determines which users consume the highest power and hence are most costly and should be transferred to WiFi.

WIFI DIMENSIONING METHOD In this section, the proposed dimensioning method for the minimum needed number of WiFi APs K is presented.

Available WiFi Capacity To calculate the WiFi network remaining capacity, we need to measure the network load or occupation level.

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The channels occupation in WiFi systems may be measured through the standard physical carrier sense mechanism Clear Channel Assessment (CCA), which listens to the received energy on the radio interface. ;;ƒ   "    …X     §;   can be decoded will cause CCA to report the medium as busy for the time required for the frame transmission to complete [12]. However instead of adopting the instant CCA info on each WiFi AP      $   ‰      $ %>   !    %%  on the channel load estimation method previously analyzed in [9], which enables to scan and measure the occupation of all WiFi overlapped physical channels simultaneously, collected on a higher control node, instead of the local measurement on each AP. This load estimation method facilitates the occupation measurements aggregation and processing time. In addition, since initially this value is an instant occupation measure, we consider in this paper the average value of channels occupation during peak hours for several days within the LTE-WiFi HetNet, so the dimensioning calculations will be based on an averaged occupation value for several days  ‰   ! ! …X  $ Let ½½   '  !%'!   !—+Ÿ½‚ +Ÿ½‚    '!! ! % …X  ! In addition, since WiFi APs operate on the different 12 channels of the 802.11n system based on the minimum load value of the channel [9],   ƒ     %  ! !   %     ! > taking into consideration that they are not neighbor APs to avoid the interchannel interference. Therefore, the total available capacity of this channel i will be divided between at least two APs. If we consider ti as the number of APs operating simultaneously under the different frequencies of the WiFi channels (1        ! S, i.e., its payoff V(S), is simply the difference of the revenue worth ‹+‚‚   ‹+‚#  ! %    of each player i as follows [14]: (10) where r and c are the revenue and cost components respectively. We now derive closed-form expressions for the Shapley value so as to ease its numerical computation and overcome the exhaustive summation in Equation (8).

Revenue Sharing Revenue depends on the pricing of data traffic offered to mobile users, and the volume of this traffic. In general, operators offer various data bundles with a flat rate for each one. Therefore, by having the total number of mobile subscribers within the LTE network, NLNL, and the number of users transferred to the WiFi network, NW, along with their related average Mbps volume per month, the operator can estimate the related revenues. Let ÆLÆw be the total average volume in Mbps per month per user connected on LTE and per user transferred to WiFi respectively. This volume is calculated based on an average value per month calculated from Equation (1). Ç % % =%%  §#  $%   Table 1.

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Table 1   Parameters Bandwidth Duration RBs per Time Slot RBs per TTI Subcarriers per RB Max eNB TX Power Max UE TX Power Symbols per RB Number of subframes (T) Block Error Rate Channel Gain Max WiFi AP Cost of LTE BS Cost of WiFi AP ;=%„# 

Values 10 MHz 10 ms 50 100 12 43 dBm 23 dBm 7 20 0.1 6 dB 600 Mbps 45,000 USD 500 USD 0.001 USD

The revenues of the network in presence of LTE only (GL), and in presence of LTE and WiFi (GL,W) are calculated as per the below equations respectively: Case where WiFi supports LTE: (11) (12) Case where WiFi does not support LTE: (13) (14) Indeed, in the scenario where WiFi does not support LTE, WiFi will not  %%  ‰  '   >    ! LTE users NL will stay connected to the LTE network, and therefore GL and GLW are equal. Revenues in presence of WiFi network only are not applicable, since this case is not considered, thus Gw=0.

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By applying the Shapley value of Equation (8), we calculate the share of both LTE and WiFi in the revenues, assuming the different permutation of the two players (LTE-WiFi, then WiFi-LTE) as per the below equations: (15) (16) are the shares in revenues of LTE and WiFi respectively.

Cost Sharing The cost of equipment and related operations expenditure for the LTE BS and WiFi AP are CLBS and CWAP respectively presented in Table 1. In addition, based on Equation (8), the cost shares of the network in presence of LTE only (CL), and in presence of LTE and WiFi (CL,W) are calculated as per the below equations: (17) (18) K is the number of WiFi APs calculated in Equation (7), and L is the number of LTE BSs that will assure an average throughput per user greater than 20 Mbps for around 100 simultaneous active users [10] (minimum values for L are considered as follow: L=1L=1 in case of WiFi support, L=2 in case WiFi does not support LTE). Similarly, the cost in presence of WiFi network only is not applicable since this case is not considered, thus CW=0. The same method based on Shapley value is applied for the cost shares of LTE and WiFi to get the below equations: (19) (20)

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126

  The profit distribution of each player is simply the difference between its revenue and cost share as per Equation (10). We consider as previously described two scenarios: -

Scenario 1: the case of a single, joint LTE/WiFi operator, where the same operator owns both LTE and WiFi infrastructures. Scenario 2: the case where the LTE and WiFi are owned by separate operators. X  > !!  %  …Xƒ%%§# for its heavy users and in case there is no WiFi support. " >   !  > '    %  share as per the below equations: (21) (22) (23)   > %   !!  % !§# and WiFi as per the below equations: (24) (25) „  > %   %! '! is appealing % '   %!   % %%!     ' !! %  #         !  ! >   %   %'   >  of WiFi support, earlier than the case of without WiFi support. This is due to the fact that the cost of investment in WiFi is much less than the additional cost of investment for the LTE BSs, with same subscribers’ revenues and offered throughput per user.

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SIMULATION RESULTS AND PERFORMANCE EVALUATION We consider in our simulations, an LTE FDD system for one eNB cell operating in 1800 MHz with an available bandwidth of 10 MHz [8] [16]. The WiFi network is based on 802.11n system that operates in 2.5 GHz bandwidth with 12 overlapped channels on the physical layer [9]. Every Transmission Time Interval (TTI), the eNB makes a scheduling decision to dynamically assign the available time-frequency RBs to the UEs. The eNB scheduler aims at power minimization while also at satisfying UEs demands. Table 1 “        !  > !  considering a total number NcNc of LTE users operating in the heterogeneous network varying from 10 to 100 users per eNB making simultaneously data sessions. }         %>  %       >  ! results by using MATLAB to analyze the minimum required number of WiFi APs versus LTE and WiFi throughput. By varying the number of simultaneous active users in the LTE cell from 10 to 100 active users, Figure 2 represents the number of users considered as

'    ‰ WiFi network. #$   §#  !! ‰    demands exceed the 20 Mbps, considered as the average per user throughput in LTE-A network [10], the minimum needed number of WiFi APs, and the acquired throughput in the WiFi network are shown in Figure 3 and Figure 4 respectively, noting that there is no restriction in this case on the maximum offered throughput per user in the WiFi network. ƒ    ' >     §#      ‰ >   …X network with only one AP can provide up to around 120 Mbps as theoretical value on top of its existing users. The WiFi throughput per user decreases   ! ‰ ! '  >   '  ˜=%>     Œ     !  LTE network (20 Mbps). By adopting this method, in addition to the saved cost when increasing the WiFi APs in indoors environment, to a maximum of APs as shown in Figure 3, instead of increasing the number of eNBs; the user experience will be enhanced instead of suffering from any possible congestion or throughput deterioration with limited number of LTE eNBs.

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Figure 2 Š        ‰   …X    %     ! number of active users in the LTE cell.

Figure 3. Total number of needed WiFi APs with no limitation on average per user WiFi throughput.

Figure 4. Average per user WiFi throughput (Mbps).

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129

If we take the scenario of a restricted threshold of throughput offered to the offloaded users in the WiFi network (e.g. a max of 20 Mbps), the needed number of WiFi APs will be reduced to 3 APs as presented in Figure 5.

Figure 5. Total number of needed WiFi APs with average per user WiFi throughput set to 20 Mbps maximum.

To pinpoint the saving in LTE when applying our proposed dimensioning method, we have measured the average power consumption saving related to the transmitted power after being transferred to WiFi. Figure 6 and Figure 7 represent the average saved power consumption in the eNB in Watts, and the percentage of power saving in respect to the total consumed power, respectively. As we can observe, there is on average 40% saving of the total consumed power in the eNB. This saving is expected    '!       ‰      ƒ  can observe the average power consumption in Figure 6 is proportional to     ‰ WiFi in Figure 2 since this power % !%…  ‰   X!!>    !    %  !!     %!  '! are presented in Figure 8 and Figure 9 for Scenario 1 (joint operator) and Scenario 2 (separate operators), respectively. The study is spread over 10 months where we only consider that the subscribers’ number is constant during this period without additional growth  §#   $ +      $ Œ%‚ "    >  consider that a minimum of 1 BS is needed for 100 simultaneous active users with WiFi network support, and 2 BSs are needed in case of no WiFi %%"X ‡>     > % §#  

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WiFi support could be the same as if there is WiFi support. We should note that, after this period, the taken assumptions of the growth of subscribers and consequently the growth of revenues and the needed number of WiFi APs will be changed, however this analysis is not considered in our paper at this stage.

Figure 6. Average power consumption saving in Watt.

Figure 7. Percentage of power consumption saving.

In case of joint operator in Figure 8, we can observe that the breakeven % % ! `      '   share become higher than the investment cost in case WiFi supports LTE. However, this gain is much more delayed for almost several additional months in case there is no WiFi support.

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Figure 8  ­…X’§#% 

Figure 9   % …X§#% 

"  % % X ‡> %' %  §#  !  ‡  >   !   %  in case of joint operator with WiFi support. ">  '   %  …X! %'    % %   X ‡>      to WiFi is directly covering the investment expenses or cost. Finally, we can conclude that the Return on Investment (ROI) is maximum in the scenario where the operator owns both WiFi and LTE networks, and while WiFi is providing support to LTE.

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CONCLUSIONS In this paper, we have proposed a mathematical approach to find the minimum required number of WiFi APs to support the heavy users’ traffic transferred from LTE to WiFi network, based on the remaining available capacity of the WiFi network. This capacity was estimated considering the overlapping characteristics of the physical channels of the WiFi technology, where we can estimate the average percentage of busy time and idle time of the channels during peak

  ' !   !!%  capacity of the WiFi network. #        %%  …X     >   %    §# !   ' ? >%       % !!%%!   %! ‹! #    %    %!  '! is maximal when the same operator owns both WiFi and LTE networks and while WiFi is supporting LTE. Furthermore, through the mathematical approach proposed in our paper,       Œ    §# while providing a high level of bit rate to the end users, and with minimum required hardware and investment cost. Further studies could be performed to include the transfer of users from WiFi to LTE to ensure both directions cooperation, and the transfer of LTE users to WiFi based on the coverage area and distance between the APs and the transferred users. In addition, we can consider the performance degradation due to hidden and exposed node problems and investigate the impact of %! % !%! !  +=;‚ levels on WiFi cell deployment. Finally, with the deployment of Internet of # > %!$   !! !!   #   > %!$    allocation in mobile networks will become also a crucial problem to solve and analyze in future studies.

     The authors declare no conflicts of interest regarding the publication of this paper.

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REFERENCES 1.

Sun, H.Y., et al. (2017) Enabling LTE and WiFi Coexisting in 5 GHz     %  š!“ Z!  ;%  Š  $ and Communications, 2017, Article ID: 5156164. https://doi. org/10.1155/2017/5156164 2. ! > ">  ! +‚ #  {‰  `^ §  {‰  Š Œ ^   Š  $>ƒ!>  !+ ‚…X{‰š „ ' #> !+‚  …X„ %! ƒ! }  on Realistic Mobility Characteristics. IEEE MASS, San Francisco, 8-12 November 2010. https://doi.org/10.1109/MASS.2010.5663941 6. „ > >  ! +‚ ; !!! #  {‰    …X Networks. IEEE MASS, Valencia, 17-22 October 2011. https://doi. org/10.1109/MASS.2011.26 7. ; >*=> !+¢‚  $=! „{‰§# Unlicensed Spectrum. IEEE Transactions on Wireless Communications, 15, 4987-5000. https://doi.org/10.1109/TWC.2016.2550038 8. ƒ%!>ƒ> !+˜‚;==! „{‰=   Networks. IEEE Global Communications Conference, Austin, 8-12 December 2014. https://doi.org/10.1109/GLOCOM.2014.7037578 9. Saliba, D., et al. (2017) Overlapped Physical Channels Load Measurement in 802.11 Networks. International Journal of Advanced Research in Computer Science, 8. https://doi.org/10.26483/ijarcs.v8i8.4810 10. Singh, K.P. and Chopra, P.K. (2014) Throughput Computation of LTE-A Network for Urban Area. International Journal of Advanced Research in Electronics and Communication Engineering, 3.

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11. Wang, Y.P., et al. (2012) Self-Optimization of Downlink Transmission Power in 3GPP LTE-A Heterogeneous Network. Vehicular Technology Conference, Quebec City, 3-6 September 2012. https://doi.org/10.1109/ VTCFall.2012.6398913 12. Tanenbaum, A.S. and Wetherall, D.J. (2010) Computer Networks. Fifth Edition. 13. !> „>  ! +‡‚ …X „   {‰ §#  `^ Networks. IEEE Computing and Communication Workshop and Conference, LAS Vegas USA, January 2019. 14. ““>ƒƒ> !+‚„   ¡ LTE/DVB Systems to Offer Mobile TV Services. IEEE Transactions on Wireless Communications, 12, 6314-6327. https://doi.org/10.1109/ TWC.2013.110813.130397 15. Shapley, L. (1953) Contributions to the Theory of Games II. Annals of Mathematics Studies Vol. 298, Princeton University Press, Princeton, Ch. A Value for n-Person Games, 307-317. https://doi. org/10.1515/9781400881970-018 16. Bertrand, P. (2011) Channel Gain Estimation from Sounding Reference Signal in LTE. IEEE 73rd Vehicular Technology Conference, Yokohama, 15-18 May 2011. https://doi.org/10.1109/VETECS.2011.5956571

CHAPTER 8

Construction of Enterprise 5G Business Ecosystem: Case Study of Huawei

Yan Li, Tianzhu Li School of Business Administration, University of Science and Technology Liaoning, Anshan, China.

ABSTRACT Existing research does not explain the construction of the 5G business ecosystem at the enterprise level. Using the method of case study, through the analysis of the development and structure of Huawei 5G business ecosystem, we explore the construction model of enterprise 5G business ecosystem. On this basis, three experiences such as clear risk permutation and combination, accurate positioning of the enterprise and replace the business ecosystem, are proposed to give some inspirations for enterprises

Citation: Li, Y. and Li, T. (2021), Construction of Enterprise 5G Business Ecosystem: Case Study of Huawei. American Journal of Industrial and Business Management, 11, 92-110. doi: 10.4236/ajibm.2021.111007. Copyright      ! "#  $!censed under the Creative Commons Attribution International License (CC BY). http:// creativecommons.org/licenses/by/4.0

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to build 5G business ecosystem. The main contribution is of reference value for the benign operation of 5G enterprises, and of practical significance for the development of 5G industry and enterprise innovation. Keywords: 5G, Business Ecosystem, Case Study

INTRODUCTION Based on growing demand for mobile data on the Internet, 5G technological innovation and its industrial development have attracted wide attention from all walks of life. In June 2019, the Ministry of Industry and Information Technology issued 5G commercial licenses to China Telecom, China Mobile, China Unicom and China Radio and Television, China officially entered the first year of 5G commercial use (Xu, 2019). 5G will become an important catalytic factor for comprehensive formation of Internet of Things, accelerating transformation from mobile Internet to mobile Internet of Things. The collaboration and mutual shaping between 5G and Internet of Things will start to bring revolutionary changes to economy and society (Palattella et al., 2016). The epoch-making significance of 5G opens up a new space for innovation in current market. Enterprises should keep up with the pace of times and build a business ecosystem with matching degree and correlation degree in order to win the opportunity in 5G competition. Rather than competition between enterprises, it is more reasonable to express the competition between enterprises in business ecosystem. Enterprises reduce the cost and risk of industrial chain through internal system management and external resource allocation, which are good to enhance the reserve of competitiveness and accelerate the pace of development. For example, Xiaomi chose ecosystem construction of smart

   !!  !    '  '!     Œ%   trial and error of enterprise expansion model (Tan et al., 2019); Apple responded to dynamic changes of consumer market and captured the needs of consumers to form business ecosystem in which enterprise itself is core (Liu & Xiong, 2013); Both structure and technological development are integrated by Alibaba to form derivative business such as terminal and then Alibaba established a business ecosystem (Tong & Yang, 2019). Academia has studied the evolution of corporate business ecosystem from different perspectives (Anggraeni et al., 2007). For example, Jingdong captured opportunities and needs in earlier stage, integrated organization in midium-term, and then released its platform advantages in later period. The improvement of business ecosystem presented a win-win situation (Zhang

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et al., 2018). Wind turbine enterprises in China have built a wide range of international licensing network and domestic cooperative relationship from capacity accumulation (Chen et al., 2014). However, there is still a lack of research on 5G business ecosystem, especially at enterprise level. There is no complete framework for enterprise `^   ƒ     Œ    the rapid development of 5G industry, the paper selects Huawei as a research case to explore the process of building enterprise 5G business ecosystem, and sublimates theoretical model and management enlightenment, which will help improve the reference value of 5G industry development and enterprise innovation.

RESEARCH DESIGN Theoretical Summary Moore (1993) drew a new metaphor of competition from the study of biological and social systems, and first proposed the concept of business ecosystem. He believes that business ecosystem that participating members work together to benefit mutually is an economic consortium based on the interaction of organizations and individuals. Most companies in business ecosystem adopt competition and cooperation to create the best economy and quality products or services that meet the needs of customers (Moore, 1993). Scholars such as Lansiti & Levien (2004) further point out that business ecosystem connects an interactive network structure to reduce the risk of system. The health of system is determined by overall operation level (Lansiti & Levien, 2004). Specifically, business ecosystem is a networked organization around core technologies, in which participants are at stake with each other. The explicit mode of interdependence between participants is an essential feature of dividing business ecosystem and departments, that is, when a participant leaves the system, the benefits of other participants are weakened; when a new participant enters the system, the benefits of all participants increase. Each element member has to bear an unknown future (Hartigh et al., 2006). There are three theoretical perspectives regarding the evolution of business ecosystem. One is the perspective of biological ecosystem. This is the initial stage of business ecosystem. Just as the survival of species depends on sunlight, water and soil nutrients in natural resources, business      ! !%!  %!>     

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and talents generated by innovation from the perspective of ecological equivalents (Moore, 1996). The second is business model perspective. This is the growth stage of business ecosystem. Some scholars believe that business model (implicit and explicit) is a new unit of analysis. The scope of company-centric business model should be broader than company’s business scope. It emphasizes explain value creation and value acquisition (Zott et al., 2011). The third is the perspective of innovation ecosystem. This is the evolution stage of business ecosystem to a higher level. Innovation ecosystem is a more complex ecosystem (Sun & Wei, 2019). Innovation changes %%> '    >          of ecosystem (Ritala & Almpanopoulou, 2017). The components of bioecosystem are inconsistent with enterprise and business model framework is not comprehensive enough, this article applies the perspective of innovation ecosystem compared with business model and biological ecosystem, which can better reveal the commercialization of enterprises. Innovation is the only sustainable source of competitive advantage for enterprises, but innovation cannot evolve in a vacuum. Enterprises need to gather funds, customers and many other elements, seizing opportunities to gather strength to achieve value creation. In 5G era, business ecosystem plays a more prominent role in corporate innovation and competitive advantage. However, existing literature lacks corresponding research on construction, evolution, and structure of corporate 5G business ecosystem.

Research Method In theoretical construction, case study has become an increasing popular strategy, which forms the basis of a large number of studies (Eisenhardt & Graebner, 2007). Comprehensive theoretical construction, theoretical sampling of cases, unbiased interviews, abundant evidence in tables or appendices and clear theoretical arguments bridge from qualitative evidence to mainstream deductive research, they are the characteristics of case study. Case study is usually regarded as useful tool for exploring research projects, it can describe the nature of things and explore overall evolutionary development of things. Case study is suitable for new or relatively weak research fields or in-depth investigation of a specific and complex case of real world (Rowley, 2002; Yin, 2013). This article studies the evolution of 5G enterprise business ecosystem, involving how enterprises build 5G business ecosystem and the cooperation within 5G business ecosystem.

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Considering that the relationship between business ecosystem construction is more complicated, case study method is a suitable choice. In specific research process, we use a descriptive single case study method to discuss 5G business ecosystem of Huawei through detailed description of practical activities, showing the growth process of 5G business ecosystem from shallow to deep over time and abstracting inductive theory laws.

Research Case and Data Collection In accordance with typical and extreme requirements of case study, we screened multiple cases and finally determined that Huawei was the subject of this case study. The main reasons are the following three. First, as the leader of Chinese private enterprises, Huawei is the world’s leading provider of information and communication technology (ICT) solutions, and has built an integrated pattern of cloud platform, industrial application and intelligent terminal. Secondly, Huawei not only has outstanding R&D capabilities and numerous patents in 5G network, but it also has manufacturing capabilities that other companies cannot match, making it a place in 5G industry. Finally, Huawei has been focusing on polishing business ecosystem. Its internal structure has been adjusted by trial and error, and its revenue model has been improving, showing a positive corporate trend which has a strong reference value for 5G related enterprises. The materials we used to construct the case mainly include annual reports obtained through public channels, various academic papers, media reports and information published on the official website of Huawei (as shown in Table 1). According to the “triangular verification” thinking model, information from different sources is compared to achieve mutual verification between the logic of data and information, so as to maximize the credibility of the research.

CASE INTRODUCTION In 1987, Huawei Technologies Co. Ltd was established in Shenzhen, Guangzhou. From early production of communication equipment to the complete layout of communication equipment business for operators, cloud services for enterprises and consumer services for terminals, Huawei business scope has continued to expand in the 30 years since its establishment. In 2009, Huawei began a planned layout of 5G technology. The specific development process is shown in Figure 1.

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Table 1. Data source table. Data source

{ !   Media coverage Huawei

Academic research achievements

Data content listing

Major events and decisionmaking content that Huawei has participated in at different stages of 5G, such as the establishment of 5G innovation research centers in 9 countries in 2014.

Search the research results of mainstream journals on Huawei, such as the article “The Importance of 5G and China’s Catching up Opportunity” published by People’s Forum.

Mainstream media’s interviews with Huawei’s management and reports on Huawei’s 5G-related press conferences, such as huawei’s Products and Solutions Press conference in London 2020.

Figure 1. Timeline of Huawei 5G major events.

In 2009, Huawei used a mathematical paper on polarization code published by Turkish professor Arikan as theoretical basis (Arikan, 2009), invested 600 million dollars to start. In order to conduct 5G basic technology research, research department has established a team of thousands of people engaged in decoding research and development, and 5G core research and development center was established in North America. In 2011, “2012 Laboratory” was born, which fully supported 5G products in terms of materials and technologies such as heat dissipation, ice and snow protection, and corrosion resistance. For example, the “Advanced Thermodynamics Research Laboratory” focused on solving the overheating problem of RUU product chips assembled with multiple modules to ensure the operating frequency of high-performance products that are differentiated from low-

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performance products. In 2012, Huawei, as a member of core management team, established METIS project alliance and worked with European partners to provide a strong support platform for the development of 5G system and technology. In 2013, Huawei released 5G white paper and formed close joint research relationships with more than 20 universities around the world, including St Petersburg University, the University of Surrey and Shanghai Z#š' !%!  !}˜>¡   !  5G innovation research centers in the United States, Germany, Japan and other countries. Huawei joined Japanese 5GMF to jointly formulate 5G development plans and cooperated with multinational operators. In 2015, Huawei was deeply involved in 5G-MoNArch, and its 5G industry cooperation made positive progress in North America, Europe and China, Huawei took responsibilities among multiple international associations and alliances. Since 2016, Huawei has worked hand in hand with multinational operators such as Telefonica and Telkomsel in Indonesia, taking another big step on the road to 5G deployment and operation. With the increasing maturity of 5G technology and the acceleration of base stations, Huawei 5G commercial volume has continued to make breakthroughs. 5G has entered the commercial journey from previous technology investment to value acquisition. In 2017, Huawei, an important member of IMT-2020 (5G) China Evaluation Group, was the

%!    %  ;   `^  !    development trial. In 2018, Huawei launched a full range of 5G product solutions that are supporting “end-to-end”. By the end of 2019, Huawei had !  !? `^! and had began to cooperate with operators to expand mobile broadband services, applications in 5G HD video and AR/VR. Huawei kept in line with consumer demand and continued testing in multiple scenarios to ensure commercial quality. In 2020, Huawei has won 91 5G commercial contracts, this number far surpassed competitors such as Ericsson and Nokia, ranking

  !¡   Œ%% $   up a highly automated wireless product factory in France to produce wireless communication equipment for European market. This is a major breakthrough in 5G European market.

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CASE ANALYSIS The Formation Process of Huawei 5G Business Ecosystem The business ecosystem of Huawei changes with the change of strategic policy. In order to meet the needs of 5G industry innovation, Huawei has carried out continuous transformation, reorganization, and has optimized its organization to build a corresponding business ecosystem. For the convenience of narration, we divide the growth process of Huawei 5G business ecosystem into three dynamic processes: Huawei initial trial period of 5G from 2009 to 2012, Huawei 5G expansion and development period from 2013 to 2017, and Huawei mature and upward phase of 5G from 2018 to the present, as shown in Table 2. 1)

Preliminary testing period. Huawei initially focused on the development of single technology and explored the formulation of 5G standards in practice, among which the “2012 Lab” platform helped Huawei in the initial stage of 5G. On the one hand, Huawei increased the training of 5G technical teams and the investment of 5G R&D funds. Resource and capital helped to promote research and development of 5G technology. On the other hand, it accelerated the global layout of 5G research centers and built a unique and innovative 5G ecosystem featuring winwin cooperation. Canadian study center, for example, gathered excellent scientists and engineers with great concentration of 5G chip development and standards at home and abroad. Huawei relied on technology and information reserves in communication industry for many years, and cooperated with many partners to promote the process of 5G. In anticipation of the possibility that 5G would become a basic social network in the future, Huawei began to accumulate energy and invested various resources such    !    to accelerate research and development process, preliminarily establishing the industry leading edge (Figure 2).

Figure 2. Huawei 5G business ecosystem in initial trial period.

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Table 2. Growth process and characteristics of Huawei 5G business ecosystem. Test period

Extended period

development Mature period

Research and The initial re- In-depth research and de- Focus on research development search and de- velopment and development of 5G velopment 5G product

5G has fewer 5G patents rank top in the 5G patents rank patents world

  !

5G corpora- A handful of Most universities and or- =!< !> tion R&D centers ganizations round

!!
 !! inter-industry cooperation, but also promote the establishment of cross-industry communication platforms. Under the premise of continuous research and development, Huawei has begun to cooperate with strategic partners such as operators and equipment vendors, looking for the pain points of different customer groups, providing more comprehensive products and services. Gradually Huawei expanded the scope of enterprise 5G business ecosystem. For example, Huaguang Technology is an important supplier of ¡  `^ %! !           %%!  of Huawei 5G base station construction. Keysight Technology is a company that mainly produces measuring instruments and

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3)

software, assisting Huawei in completing the second phase of 5G technology testing. Huawei 5G business ecosystem was improving day by day in cross-industry cooperation (Figure 3). Rise in maturity. Huawei has tested in 5G technology, standard,  %>  $> !  !>! `^  ! a new level of speed and stability. Since 2018, Huawei has put forward 5G proposition of industry leading, technology leading and experience leading. 5G microwave starts a new journey for full commercial use. 5G entered the stage of productization. Huawei 5G mobile phones and base stations were important embodiment of the commercialization of baseband chip technology. Promoting 5G commercialization can accelerate completion of 5G technology research and development, and promote further maturity of ecosystem of chip, component, and terminal. During this period, ¡  `^ !!    !   %   products to select partners. As core enterprise, a clear strategic goal of building business ecosystem is compatible with 5G-related enterprises (equipment vendors, operators, etc.) and users. For example, 5G mobile phones used Huawei self-developed baseband chips named Barong 5000 and Kirin 980. Huawei cooperated with BYD Electronics, Goertek and other companies in professional

!   !  +§Œ  ‚   %! technology is the world’s leading (Figure 4).

Figure 3. Huawei 5G business ecosystem in expansion and development period.

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Figure 4. Huawei 5G business ecosystem in the mature and rising stage.

The Structure of Huawei 5G Business Ecosystem More than ten years of R&D investment and establishment of 5G business ecosystem are key factors for Huawei to become a leader in the global 5G market. Huawei 5G business structure is generally divided into four major categories: operators, enterprises, consumers, and “Cloud + AI”. Each part has a clear division of labor, strong professionalism, high cooperation efficiency and significant value creation. This structure fully demonstrates the cohesion of resources and core competitiveness (Figure 5). 1)

Huawei operator 5G business ecosystem. Huawei operator 5G business relies on operators with competitive advantages and attracts a large number of 5G-related companies into ecosystem, which is plenty of room for cooperation. Traditional communication services were limited by transmission speed and delay time. Service content is single and costly, which not only fails to meet the new demands of consumers, but also restricts the collaboration methods of participants. Facing the characteristics of 5G data extremely fast transmission, stable connection and massive IoT, Huawei sliced 5G network, divided resources, and provided packaged products according to different demands with different billing methods. In the past few years, Huawei and Vodafone and other top global operators established

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2)

3)

joint innovation centers and expert seminars based on common philosophy of pursuing innovation and excellence, aiming to                     " 2020, Huawei launched “5G Partner Innovation Program”, which '!'   > > !  !ƒ%%! '%% `^  $     operators, it is also a new cycle of network construction recovery. Huawei Enterprise 5G Business Ecosystem. Huawei enterprise business covers many areas and provides differentiated solutions for different customers. During 5G era, Huawei corporate business extends to more industries, builds a cross-industry communication platform, establishes a targeted network architecture, strengthens dialogue and understanding with vertical industries, and balances the relationship between the goals of business ecosystem members and their own development goals. For example, Huawei, Quectel, China Mobile, Changhong and Sichuan Ailian jointly released 5G modules. Huawei was responsible for main part of module   !%    !     %  ¡  ! `^   > %  with 18 auto companies such as FAW Group and BYD. Huawei  !   %      !  ! seeking new opportunities. Huawei consumer 5G business ecosystem. Huawei has formed its own brand market in terms of user payment, service, experience, implementing “1 + 8 + N” full-scenario smart strategy. Huawei self-produced products represent Huawei’s high share of mobile phone market. The advantages are extended to the periphery of tablets, PCs, wearables, smart screens, AI speakers, headsets, VR and car machines. The starting point of Internet of everything is connection, which is also starting point for competition among various manufacturers. Huawei 5G platform responds to market rules and cleverly connects the needs of various industries so that participants can more quickly capture opportunities. For example, Huawei had 5G chip technology and had long-term cooperation with Changxin Technology and Kodali, etc. Based on the needs of consumers, Huawei continuously optimizes and upgrades products and adhere to the R&D investment of cooperative innovation. Huawei continues to break through consumers’ current expectations and leads the trend of global consumer smart

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terminals. Through the collaborative innovation of two platforms of internal and external ecosystems, Huawei intelligently matches users’ demands with terminal service capabilities, so as to enjoy the wonderful experience of smart life.

Figure 5. Huawei 5G business ecosystem.

4)

Huawei “Cloud + AI” 5G business ecosystem. In the context of 5G, “Cloud + AI” business has been put on the agenda in recent years, it provides smart city solutions in areas such as urban operations, unmanned driving, and medical care. As of 2017, Huawei built a Cloud platform with openness and high credibility. In 2019, Huawei launched 5G medical technology cooperation with International Medicine and China Mobile to help telemedicine services in 5G era. In the same year, Tianyuan Technology and Huawei jointly contributed to the application of 5G in remote control of construction machinery. During the novel Coronavirus outbreak, Huawei Cloud “WeLink” opened  !   !>  !! interacting in medical teaching. 5G high-speed transportation data provided feedback, so that experts could ask for consultation in time and family members could visit remotely to avoid the risk of infection. The integrated development of “5G + Cloud + AI” technology will become a new driving force for industrial  ' !% ;!%' ‰ Œ!  !>

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which reduces operating costs. In short, by building a business ecosystem with its own advantages, Huawei continues to innovate and actively develops a Cloud ecosystem of cooperative research development and regional synergy to accelerate the deployment of Huawei’s competitive position in 5G industry.

DISCUSSION AND ENLIGHTENMENT Enterprise 5G Business Ecosystem Construction Model Throughout the literature at home and abroad, the academic circle has formed a relatively comprehensive cognition on the business ecosystem. Although the process and path are different, they pursue the same goal and benefit, and eventually derive into a network structure system with close interaction and stable compatibility. Traditional communication industry has formed a longterm fixed model in face of relatively stable user groups and suppliers, and 5G, as the backbone of the latest mobile communication wave, has the ability to realize three application scenarios of large broadband, low delay and wide connection, which promote cellular mobile communication technology to a new height. 5G technology that truly changes the course of society breaks through original competition pattern and squeezes original profit space. It is difficult to try a familiar field in an unfamiliar edge, but Huawei has gradually explored a business ecosystem suitable for the development of 5G, made clear market positioning, and achieved great success. Based on the analysis of Huawei 5G business ecosystem, we propose that companies need to play a role of resource integrators who can rely on their own advantages to implement a series of strategic changes to corporate platform system and create an internal and external environment in which each organizational link plays its own role (Figure 6). Enterprise 5G business ecosystem emerged in networked enterprise organization, and 5G resources can be quickly transferred in networked structure. We should focus on the broad operational characteristics of the   ! >  %     and ecosystem “health”. The evolution literature suggests both that the capacity for creating novel functions is an important measure of system health, and, critically, that the process of integration is an important way of achieving such novelty. The collective impact of network interactions   %% % %         fundamental question of ecosystem health. Focusing on the behavior of

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individual organizations and developing some basic capabilities for successful implementation of strategies for different types of ecosystem strategies is the most important part of networked industry management (Iansiti & Lewin, 2010). Through enterprise 5G platform system, 5G device manufacturing %%!  %'  %!  >   >  %     '  With the large-scale construction of 5G network guaranteed by operators, enterprises can open sales channels in such a layout, which can not only provide ordinary consumers with 5G products closely related to improving the quality of life, but also create professional 5G components for customized users. In the enterprise 5G business ecosystem, enterprises play more than  ! > '   Œ   % `^%!  closely connects consumers, 5G manufacturing suppliers and 5G operators together. Effective division of labor reduces time costs and resource costs. The process from parts to products is convenient, so that consumers get high-quality products and enterprises get rich returns. Game rules change  !  < !     >   <      % # ? why enterprises are not abandoned by the game circle.

Figure 6. Enterprise 5G business ecosystem.

1)

5G device manufacturing supplier. 5G MIMO technology   ' ! %'          anti-interference performance. Therefore, the replacement of base station antennas is extremely important. Huawei, Tongyu Communications, Jingxin Communications and other companies

'  '!'   !  „   construction methods of 5G carrier network have put forward higher requirements for professional performance of optical

  ; %      $    %   %'   !> and obtaining long-term and stable supply is the most reassuring guarantee for enterprises. Companies such as YOFC and

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2)

X ¡      %%!  %!   and cable. At present, Qualcomm and Huawei HiSilicon are the  %'   `^    % #  !    `^ device manufacturing suppliers needs to measure transaction cost of external market and operating cost of internal system. There are information exchange barriers between enterprises, and technical performance may not be well-known in the market. Effective communication between enterprises is an important means to break the barriers. It is a crucial strategic decision to make full use of strengths and circumvent weaknesses according to the degree of specialization (Barney, 1999), Make full use of what the enterprise is good at and seek the cooperation of the enterprises that are not good at. For the purpose of seeking advantages and avoiding disadvantages, enterprises will spontaneously organize, interact and cooperate without external or internal leaders (Peltoniemi & Vuori, 2004). 5G operators. Chinese three major operators, China Mobile, China Telecom and China Unicom, are accelerating their deployment by the booming wave of 5G. At the beginning of 5G commercial use, operators have carried out large-scale network construction. In 2020, 5G networks were fully launched, and the three major operators intensively issued large purchase orders. In addition to continuing efforts in the outdoor 5G Acer Station, indoor 5G micro-base station will become the focus of the three major operators this year. China Telecom and China Unicom will share indoor 5G and accelerate the integrated deployment of indoor scenes. Operators speed up 5G construction and application innovation, carry out 5G network coverage in some hot areas, cooperate with 5G enterprises to integrate operators, and build a win-win cooperative development situation for enterprises. 5G enterprises have a bright future. With the application of operators’        % '  '  %% enterprises to consolidate their weak links in system, so        ''          environment. The user in the business ecosystem is a relative concept. If any enterprise refuses to cooperate and destroys  %%! >  !  !!  \ " necessary to ensure that each participating company receives a separate surplus of products or services to achieve a win-win situation for 5G business ecosystem. In the establishment of 5G business ecosystem, enterprises should not only consider their own   >!$     % > component manufacturing suppliers and other related enterprises in close cooperation. The value acquisition of individual groups is an important prerequisite for the operation of 5G commercial ecosystem. Therefore, the construction of enterprise 5G business ecosystem is by no means a blind act. Logic should be used to dominate actions, risk portfolio factors should be considered in the actual environment, and the internal and external ecosystem of enterprise 5G should cooperate together, so that the value proposition can be realized. Identify company’s positioning. In the construction of business ecosystem, more attention is paid to the traditional horizontal route from suppliers to producers, while the vertical connection between complementary enterprises is neglected. This blind spot, however, is the hinge of the decisive business ecosystem and the core link of its value blueprint. To outline the location of each necessary participant, an enterprise needs to know its  %> ! %!>   !  >  kind of suppliers are needed, and how many intermediaries are involved before the product or service reaches the end user, and what kind of risks they will face. An enterprise needs to objectively evaluate its own strength level and professional ability that is an important factor in measuring the possibility of an enterprise as a needy. In 5G business ecosystem, whether a 5G !   % !   `^!! !

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is largely due to the positioning of an enterprise in the network framework. Collaboration with different companies may result in    š  %%  5G business ecosystem is the key to making a decision (Iansiti & § ' >˜‚X Œ%! >È#%   % of 5G innovation, invested in research and development of 5G technology, and established a dedicated 5G industry product line. As a supplier of equipment and solutions, it does not directly develop 5G industry application products, but jointly develop with operators and partners in various vertical industries to ! Œ%!    `^ !  Œ       breadth. Therefore, when enterprises are participants in building 5G business ecosystem, the chances of success will be greatly improved by understanding the business ecosystem from the perspective of market structure and positioning. 3) Reset business ecosystem. The so-called reset is to accept the !         %%  % >  seek a power to combine the imperfections, and to arrange and combine the original enterprises into a new mode of action. Based on the realization form of value blueprint, resetting can be    !!  ' % % ' +X ‚ Divide the uniqueness of enterprise, create new individual value and promote the development of overall value. Companies should clearly grasp      %%> >     Œ   can bring to business ecosystem, whether they have the potential to create %   !  #   %   !   \!'   %    achieve the goal of utility maximization. Among 5G companies, there are both dominant and niche companies. The current situation is no longer applicable to the era of the dominant. In fact, professional niche companies are also capable of assuming more responsibility for value creation. ;      !“      %    are the norm in 5G business ecosystem. Companies need to leverage their competitive advantages, plan and foresee the future. In nature, one of reasons for the longevity of communities is cooperation between species. Similarly, 5G business ecosystem is usually built on a framework with extremely high corporate relevance, and the tacit understanding and communication of partners can be seen as the engine of entire system. The value of 5G business ecosystem is always more than the sum of its various parts. “1 + 1 > 2” is the result of interaction between different companies. This is

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especially evident in R&D. Cooperation and mutual understanding between enterprises on the basis of full understanding and trust will bring the role of business ecosystem into full play. To move the enterprise to a new location, change the original structure, strengthen the effectiveness, that is, to do the right thing in the right location, adaptability needs to be emphasized. First, companies must have a comprehensive understanding of actual market environment before determining business ecosystem and its environment, and then create a suitable business ecosystem framework. Enterprise boundaries can be        `^ %%!>   <  %  %  % can be re-examined. Some enterprises with the ability to create value but inconsistent positioning are faced with the risk of being squeezed out of the market by competitors, so they need to change their mobile position more.

Figure 7    '  !  `^   .

Replenish value-creating companies to enter business ecosystem and

!% !      %          !“   detail. According to the existing characteristics of 5G, enterprises should build their own business ecosystem, which should be highly sensitive and to some extent be able to accept enterprises that are more compatible with themselves at any time. It also needs to have the basic elements for the ''!`^# !              !!   ? ' !!  Cut certain companies to introduce more dynamic companies to business ecosystem. Enterprises should effectively allocate limited resources to more  !    !$ %'     ! ' !

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of enterprise value creation. The evolution of business ecosystem has been continuing, new connections have been being created and old connections are gradually dying out. 5G related enterprises, which lag behind the development trend of communication and cannot continue to create value, should be eliminated in a timely manner and stop losses in order to prevent the interests of other related enterprises in 5G business ecosystem from being threatened.

RESEARCH CONCLUSIONS AND THEORETICAL CONTRIBUTIONS This paper abstracts and summarizes the theoretical concept of enterprise 5G business ecosystem from the concept of business ecosystem, that is, placed in 5G industry, enterprises with different roles but similar interests interact on the premise of referring to internal and external factors in order to create value maximization, so as to create a situation of collaborative evolution among enterprises. This article has the following theoretical contributions to the research of enterprise 5G business ecosystem. First, it analyzes and describes the construction process of the 5G business ecosystem of the enterprise, and reviews the development process and characteristics of Huawei 5G business ecosystem from the perspective of time sequence. Introducing the structure of Huawei 5G business ecosystem, the dynamic trend is obvious. In fact, the internal logic of the 5G commercial ecosystem is that ecology is calling for the power of “integration”. Previous studies tend to be static analysis, which can no longer satisfy the need to build an ecological optimization and integration model and enable the 5G industry of all walks of life. Secondly, the experience and enlightenment for perfect integration of enterprise 5G business ecosystem is better. Academic community has certified the superiority of business ecosystem for a long time, but has failed to answer in depth how to integrate a benign business ecosystem. Huawei 5G business ecosystem has triggered thinking about this issue. Enterprise resource synergies determine how far a business ecosystem can go. Clarifying risks is the prerequisite for consideration, finding the right positioning is the direction of force. “Divide”, “combine”, “move”, “supplement” and “delete” as leverage tools assist in gradual development of enterprise 5G business ecosystem and find feasible solutions to make up for it. It fills the gap of current research. Huawei 5G business ecosystem provides the value of case references for the sound operation of other 5G-related companies, and this is a collision

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between tradition and new trends. Although corporate business ecosystem  !         “             participants, too much intervention will make business ecosystem lack of vitality, which is not conducive to the progress of business ecosystem in long run. Huawei 5G business ecosystem has fully utilized the autonomous potential of business ecosystem and carried out cooperation from internal to external, which is worth learning from other companies. There are still some shortcomings in this article: the use of Huawei single case study on the construction and experience of enterprise 5G business ecosystem may be constrained by industry characteristics, and there may be other forms of expression in different industries, so the case is descriptive rather than conclusion, awaiting further research.

     The authors declare no conflicts of interest regarding the publication of this paper.

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Adner, R. (2006). Match Your Innovation Strategy to Your Innovation Ecosystem. Harvard Business Review, 84, 98-107. 2. Adner, R. (2014). Panorama Strategy: The Ecology and Risks of Enterprise Innovation. Trans. Nanjing: Yilin Press. 3. Anggraeni, E., Hartigh, E., & Zegveld, M. (2007). Business Ecosystem as a Perspective for Studying the Relations between Firms and Their Business Networks. ECCON, 2007 Annual Meeting, 1-25. 4. Arikan, E. (2009). Channel Polarization: A Method for Constructing Capacity-Achieving Codes for Symmetric Binary-Input Memoryless Channels. IEEE Transactions on Information Theory, 55, 3051-3073. https://doi.org/10.1109/TIT.2009.2021379 5. Barney, J. B. (1999). How A Firm’s Capabilities Affect Boundary Decisions. Sloan Management Review, 40, 137-145. 6. Chen, Y. T., Rong, K., Xue, L., & Luo, L. J. (2014). Evolution of Collaborative Innovation Network in China’s Wind Turbine Manufacturing Industry. International Journal of Technology Management, 65, 262-299. https://doi.org/10.1504/IJTM.2014.060954 7. Clarysse, B., Wrigh,t M., Bruneel, J. et al. (2014). Creating Value in Ecosystems: Crossing the Chasm between Knowledge and Business Ecosystems. Research Policy, 43, 1164-1176. https://doi.org/10.1016/j. respol.2014.04.014 8. Eisenhardt, K. M., & Graebner, M. E. (2007). Theory Building from Cases: Opportunities and Challenges. The Academy of Management Journal, 50, 25-32. https://doi.org/10.5465/amj.2007.24160888 9. Hartigh, E. D., Tol, M., & Visscher, W. (2006). The Health Measurement of a Business Ecosystem: European Network on Chaos and Complexity Research and Management Practice Meeting. In J. P. Xu, M. Yasinzai, & B. Lev, (Eds.), The 6th International Conference on Management Science and Engineering Management (pp. 1-39). London: SpringerVerlag. 10. Iansiti, M., & Levien R. (2004). Strategy as Ecology. Harvard Business Review, 82, 68-81. 11. Iansiti, M., & Lewin, R. (2010). Keystones and Dominators: Framing the Operational Dynamics of Business Ecosystems. Boston: Harvard Business School Press.

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12. Lansiti, M., & Levien, R. (2004). The Keystone Advantage: What the New Dynamics of Business Ecosystems Mean for Strategy, Innovation, and Sustainability. Boston, MA: Harvard Business School Press. 13. Liu, G., & Xiong, L. F. (2013). Dynamic Consumer Demand Response, Enterprise Boundary Selection and Business Ecosystem Construction—Based on a Case Study of Apple Inc. China Industrial Economy, No. 5, 122-134. 14. Moore, J. F. (1993). Predators and Prey: A New Ecology of Competition. Harvard Business Review, 71, 75-86. 15. Moore, J. F. (1996). The Death of Competition: Leadership and Strategy in the Age of Business Ecosystems. New York, NY: Wiley Harper Business. 16. Palattella, M. R., Dohler, M., Grieco, A., Rizzo, G., Torsner, J., Engel, T. et al. (2016). Internet of Things in the 5G Era: Enablers, Architecture, and Business Models. IEEE Journal on Selected Areas in Communications, 34, 510-527. https://doi.org/10.1109/JSAC.2016.2525418 17. Peltoniemi, M., & Vuori, E. K. (2004). Business Ecosystem as the New Approach to Complex Adaptive Business Environments. In M. Seppä, M. Hannula, A. M. Järvelin., J. Kujala, M. Ruohonen, & T. Tiainen, Frontiers of E-Business Research 2004 (pp. 267-281). Tampere: University of Tampere. 18. Ritala, P., & Almpanopoulou, A. (2017). In Defense of ‘eco’ in Innovation Ecosystem. Technovation, 60-61, 39-42. https://doi. org/10.1016/j.technovation.2017.01.004 19. Rowley, J. (2002). Using Case Studies in Research. Management Research News, 25, 16-27. https://doi.org/10.1108/01409170210782990 20. Sun, C., & Wei, J. (2019). Research on the Structure and Collaborative Mechanism of Enterprise-level Innovation Ecosystem. Studies in Science of Science, 37, 1316-1325. 21. Tan, Z. J., Wei, W., & Zhu, W. X. (2019). The Construction and Value Creation of Business Ecosystem—A Case Study of Xiaomi Intelligent Hardware Ecological Chain. Management Review, 31, 172-185. 22. Tong, Y. Q., & Yang, Y. J. (2019). Research on Ali Business Ecosystem and Platform Operation Mode. Science and Technology Management Research, 39, 254-260. 23. Xu, S. (2019). 5G Contributes to High-Quality Development of China’s Economy. China Economic and Trade Guide, No. 17, 51-54.

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24. Yin, R. K. (2013). Validity and Generalization in Future Case Study Evaluations. Evaluation, 19, 321-332. https://doi. org/10.1177/1356389013497081 25. Zhang, Y., Liu R. H., & Chen H. Q. (2018). The Formation Path of Platform Enterprises’ Ecological Advantages in the Business Ecosystem—A Longitudinal Case Study Based on JingDong. Economics and Management Research, 39, 114-124. 26. Zott, C., Amit, R. H., & Massa, L. (2011). The Business Model: Recent Developments and Future Research. Journal of Management, 37, 10191042. https://doi.org/10.1177/0149206311406265

CHAPTER 9

5G New Radio Prototype Implementation Based on SDR

Lama Y. Hosni1, Ahmed Y. Farid1, Abdelrahman A. Elsaadany1, Mahammad A. Safwat2 1 Department of Electronics & Communication Engineering, Misr International University, Cairo, Egypt. 2 National Telecommunication Institute, Cairo, Egypt.

ABSTRACT The fifth generation (5G) New Radio (NR) has been developed to provide significant improvements in scalability, flexibility, and efficiency in terms of power usage and spectrum as well. To meet the 5G vision, service and performance requirements, various candidate technologies have been proposed in 5G new radio; some are extensions of 4G and, some are developed

Citation: Hosni, L. , Farid, A. , Elsaadany, A. and Safwat, M. (2020), 5G New Radio Prototype Implementation Based on SDR.Communications and Network,12, 1-27. doi:10.4236/cn.2020.121001. Copyright      ! "#  $!censed under the Creative Commons Attribution International License (CC BY). http:// creativecommons.org/licenses/by/4.0

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explicitly for 5G. These candidate technologies include non-Orthogonal Multiple Access (NOMA), and Low Density Parity Check (LDPC) channel coding. In addition, deploying software defined radio (SDR) instead of traditional hardware modules. In this paper we build an open source SDRbased platform to realize the transceiver of the physical downlink shared channel (PDSCH) of 5G NR according to Third Generation Partnership Project (3GPP) standard. We provide a prototype for pairing between two 5G users using NOMA technique. In addition, a suitable design for LDPC channel coding is performed. The intermediate stage of segmentation, rate matching and interleaving are also carried out in order to realize a standard NR frame. Finally, experiments are carried out in both simulation and real time scenario on the designed 5G NR for the purpose of system performance evaluation, and to demonstrate its potential in meeting future 5G mobile network challenges. Keywords: `^>Š >  „  

INTRODUCTION With the demanding requirements being placed upon the new fifth generation (5G) mobile communications standard, a totally new radio interface and radio access network has been developed. The 5th generation wireless access technology, which is known as New Radio (NR), could meet the growing needs for mobile connectivity. The development of the 5G NR or 5G New Radio is the way to enable   `^ !    $  $   %'        advantages when compared to fourth generation (4G). 5G NR has been developed with the aim of taking the requirements and looking at the best technologies and techniques that will be available when 5G starts to be deployed. 5G NR follows Third Generation Partnership Group (3GPP) series of standards similar to Global System for Mobile (GSM), Universal Mobile Telecommunication System (UMTS) and Long Term Evolution (LTE) [1]. ^“   ' !%%  `^Š"„  > X %   '    %!    `^ %!   equipment (UE) depends on existing LTE for initial access and mobility. So it is called “Non-Standalone (NSA) version”. In June 2018, “standalone (SA)” ' `^Š% ? '   !“    $ %   of LTE. According to International Telecommunication Union (ITU) [2] [3],

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there are three different use cases of 5G NR technology: Enhanced Mobile Broadband (eMBB), Massive machine type communications (mMTC) and Ultra Reliable Low Latency Communication (URLLC). The requirements currently being discussed for 5G are a 1000-times gain in capacity targeting very high data rates of up to 10 Gbps. Fast machine control loops and wireless emergency stop functionality require delays below 1 ms. With the number of sensors increasing, the energy costs     '   '!“ >  ¨!  !  can be obtained and a reduction in the energy cost per bit can be achieved by a factor of 100 to a 1000-fold. To meet massive connectivity demands for IoT, the connectivity density increases to be ten times higher than that of 4G; latency of 5G is also expected to be as low as 1 ms, and the cost to be  !     ˜^# !!!   is expected to reach 69 exabytes per month by 2022 at a compound annual growth rate of 45 percent [4]. These stringent requirements should stand on advanced solutions at entire 5G layers especially physical layer (5G NR). The following solutions are proposed to be conceived in 5G NR. 1)

From OMA to NOMA:- Over the past few decades, wireless communication systems have witnessed a “revolution” in      !%!      \  %  !!>  ^> 2G, 3G, and 4G wireless communication systems, frequency division multiple access (FDMA), time division multiple access (TDMA), code division multiple access (CDMA), and orthogonal frequency division multiple access (OFDMA) have been used as the corresponding key multiple access technologies, respectively. From the perspective of their design principles, these multiple access schemes belong to the category of orthogonal multiple access (OMA), where the wireless resources are orthogonally allocated to multiple users in the time-, frequency-, code-domain or according in fact based on their combinations. We might collectively refer to these domains as “resources”. In this way the users’ information bearing signals can be readily separated at a ! %! Œ %! !' !<     '  However, the number of supported users is limited by the number of available orthogonal resources in OMA. Another problem is that, despite the use of orthogonal time-, frequency- or codedomain resources, the channel-induced impairments almost invariably destroy their orthogonality. The basic goal for nonorthogonal multiple access (NOMA) is to support more users

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‚



3)

than the available resources (time- frequency- code-domains). This increase in users over resources by Non-orthogonal multiple access is adopted by the ultimate cost at receiver complexity due to requirement for separting the non-orthogonal signal. There are several NOMA solutions have been actively investigated, which in general can be divided into, power-domain NOMA, codedomain NOMA and pattern based. #   „    “ >    !!–{ '   % ˜“ #  ˜  Œ   by using “array subset” block with index zero and length of 24. Fourthly, in order to continue the long division process, repeating the previous three steps is essential. Another iteration is used which is equivalent to the size of the data without the padded bits. The resulted bits generated from the previous XOR step is used as an input to iteration loop. It is stored in a feedback node-as an initial value—which acts as a register for keeping the last value of the CRC that represents the remainder. These bits are XOR-ed with the divisor if the MSB of the remainder is one, or XOR-ed with zeros of it is zero. In each step, a bit is appended from the data to the remainder at the !  #  %%!    ˜ in the input data and incrementing the position of the bit in each iteration in order to insert all the bits inside the data to the remainder.

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Figure 1. LabVIEW implementation of cyclic redundancy check encoder of gCRC24A.

Finally, the resulted remainder (CRC) bits are added to the data replacing the padded bits. And the rate is calculated by dividing the size (number of bits) of the data by the total number of bits which is the data and the CRC bits.Cyclic Redundancy Check Decoder: The decoder CRC is the same as the encoder but with extra blocks that check the CRC value as shown in Figure 2. When the CRC bits generated from the received data are all zeros then there is no error detected in this segment. If there is an error, the receiver discards the data segment. This check is done by OR-ing all of the CRC bits generated by the receiver together. If there is any bit that is not equal to zero in the CRC then; the output will not be zero. The OR output is then inserted as a case selector to the case structure. If the CRC was correct then; the receiver will accept the data without the extra CRC bits. If not then; the receiver will not accept the data.

Code Block Segmentation After attaching CRC sequence, the data is prepared for channel coding by code block segmentation stage. Code Block segmentation is an essential process in modern mobile communication systems as the capacity of data has increased so the concept of segmentation has been applied to 4G and 5G before Turbo code and LDPC stages respectively. The segmentation processes are done when the transport block size is large. So segmentation splits it into a number of code blocks. This prepares the data for being coded using LDPC encoder. The number of code block segments depends on the maximum code block size of the LDPC coder K. Let the input bits sequence to code block segmentation stage are denoted by b0, b1, b2, ···, b}Ÿ, where B > 0 is the length of bit sequence. Each base graph (BG) in LDPC stage has its maximum code block size K.

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Figure 2. LabVIEW implementation of cyclic redundancy check decoder of gcrc24a.

Let the output of code block segmentation is B’ (the code block size). If the input bit sequence B to the code block segmentation is larger than K (B > K), the input sequence is segmented and additional CRC sequence of L = 24 is attached. Then B’ = B + CL where C is the number of code blocks. So (2) We make “ceil” for the division to round the result up to the next largest integer. On the other hand, if B < K, then no segmentation is performed }©}?#    “     !$ %  the selection of the LDPC BG. If BG 1 is selected then the maximum code block size K = 8448, which is the concatenation of segmented data bits and 24 parity bits. The parity are generated using gCRC24B !!    if needed. (3) The addition of parity bits to each code block increases the reliability of sent data and gives up to 10% of better bit error rate (BER). For BG 2 the maximum code block size is K = 3840 is used. Let Kb denote the number of information circulant columns in BG 1 or 2. For BG 1 Kb = 22, while for BG 2 Kb is set as follow: Kb = 10 if transport block size is larger than 640 bits, Kb = 9 if transport block size is larger than 560 bits and Kb = 8 if transport block size is larger than 192 bits, else than that, Kb = 6 [24]. LabVIEW Implementation of Segmentation: The segmentation process is implemented using LabVIEW by determining the length of information bit sequence B and the number of code blocks C. Then the block of CRC24B

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  % !! +  ‚ !! of information bits and K. A de-segmentation block is also implemented to retrieve the segmented blocks and check for any errors from the sent parity bits.

Low Density Parity Check Coding According to ITU-R, the performance requirements for 5G communication can be classified into three typical usage scenarios: eMBB, ultra-reliable low latency communication (URLLC), and massive machine type communications (mMTC). Applications related to URLLC and mMTC are sensitive to the latency and so they require short data package with more reliable communication. While eMBB is the most outstanding extension of 4G LTE and considers the most critical challenge in 5G scenarios to meet the continuous increase in end users demands. Channel coding is one of key technologies expected to satisfy the demands of eMBB scenario and needs to support a much wider range of code lengths, code rates, and modulation schemes than 4G LTE. In particular, the eMBB code lengths range from 100 bits to 8000 bits with code rates range from 1/5 to 8/9 according to 3GPP recommendation [24]. The required throughput with target codes should reach to 20 Gbps with block error rate not more than 10Ÿ˜. LDPC, Turbo [25] and Polar codes [26] are examples of such channel coding schemes with capacity approaching 5G requirements at the large code lengths. After the comprehensive assessment of achievable throughput, error-correcting performance, processing complexity, and processing energy consumption, QC-LDPC codes are accepted by 3GPP as the channel coding scheme for 5G eMBB data channel [27]. 3GPP has agreed to consider two rate-compatible base graphs, BG1 and BG2, for the channel coding. BG1 is   ! !$!  +`Ì‘Ì ˜˜ ‚   +’ÌÌ  ’‡‚>  }^  !! !$!  +˜Ì‘́ ˜‚ !  +’`ÌÌ’‚ Œ%!   „ %!;;§„; ! `^Š   %%¡ƒ* In the following the base graph and the exponent graph of QCLDPC is  ‰%    Base Matrix of Standard 5G LDPC Codes: The base matrices of QCLDPC code as shown in Figure 3 are constructed based on the superposition method. " '  # systematics bits exist in sub matrix A. A parity bits exist in B sub matrix which is a square matrix with bi-diagonal  #  !}  > Œ% 

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 !   ! %%    %  $ Œ   generated using “the Base graph block” as shown in Figure 4. The values of BG1 and BG2 are inserted manually according to Tables 5.3.2-2 (BG1) and 5.3.2-3 (BG2) given in 3GPP document [19]. Exponent matrix is generated by implementing modulo arithmetic with j and a to get the value of Z. The output of base graph block is fed into “LDPC code encoder block”. In LDPC code encoder the parity check matrix is converted to generator matrix by multiplying it with information bit stream. In the receiver side, the LDPC decoder extracts the bit stream by using the same parity check matrix which is used at encoder as shown in Figure 5.

Rate Matching In this section, we present the NR rate matching design and frame structure. After LDPC stage, some parity bits are punctured and zero padding bits are erased in order to shorten the code block. At the receiver, these punctured and shortened bits are added again before LDPC stage in order that LDPC could successfully decode the received bits. The punctured bits involve  %—  %   !!>   partial of added parity bits from right to left. The amount of punctured parity bits depends on coding rate as shown in Figure 6. Given the dimension of exponent matrix is nb × mb as illustrated in Figure 7, the punctured bits can be calculated as: #  %  %Šp1 = 2Z; The number of shortened bits Ns = KbÈŸ‘— The number of punctured bits of second parts Np2 = nbÈŸÈŸŠŸŠs.

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Where N is the length of code block after removing shorten and punctured bits as shown in bottom of Figure 7.

Figure 4. (a) Base graph generation; (b) LDPC encoder.

Figure 5. LDPC decoder.

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Figure 6. Base matrices of rate-1/3 (red solid line) and rate-1/5 (red dashed line) 5G-NR LDPC code.

Figure 7. Punctured and shortened bits in 5G rate matching.

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Interleaving Interleaving in LDPC code is essential as it spreads out the concentrated errors produced from the channel at the output of the LDPC decoder effectively. This technique improves the BER. Algorithm 1 illustrates the 5G interleaving process for PDSCH based on the 3GPP release. Algorithm 1. Interleaving.

Let the input bits sequence to the interleaver are denoted by e0, e1, e2, ···, eŸ, where E is the length of bit sequence. While the interleaved bit sequence are f0, f1, f2, ···, fŸ. The modulation order is Qm. Then the equation for calculating the index of the interleaved bits is illustrated in Algorithm 1. The LabVIEW code of interleaver which implements Algorithm 1 is shown in Figure 8. The outer loop’s iteration represents j which starts from zero to E/(QmŸ‚… !   !%>   % i which starts from zero to QmŸ} %!i, j in previous equation, we get fi+j · Qm = f0, f1, f2, ···, fŸ. The calculated index is then inserted to “index array” function to extract the interleaved bit. The output interleaved bits are concatenated to form the interleaved output array. The de-interleaving process reverts the original order of each interleaved bit. This process depends on a simple equation to extract the original index before interleaving. Algorithm 2 illustrates how to revert the original bits. Figure 9 shows the LabVIEW implementation of Algorithm 2. As the interleaving process, the de-interleaving is implemented by changing the equation of the index. The iteration of the outer loop j starts from zero to

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QmŸ !   !%i starts from zero to E/(QmŸ‚#  Œ calculated through the equation: ei.Qm+j where e(j.E/Qm)+i = e0, e1, e2, ···, eŸ. This index is then inserted to the “index array” function to get the de-interleaved bit.

Scrambling The data scrambling process is done by encoding the sent message from the transmitter, to make the message unintelligible for the receiver that is not equipped with an appropriately set descrambling device or decoding algorithm needed to decode the received message. The scrambling process in 5G is almost similar to that in LTE. We use pseudo-random sequence c(i) of gold code. Gold code is a code generated by XOR-ing two m-sequences, each m-sequence is obtained using Fibonacci §X>  !     power should be allocated to user with lower channel gain (QPSK user), while less power is allocated to user with higher channel gain (16 QAM  ‚§ ½  % !!    ! +¢ *ƒ= ‚>+Ÿ½‚  % !!!   ! user (QPSK user). The proper allocation of signal power for both users guarantees the QPSK user successfully decodes his signal. On the other hand, to decode 16 QAM signal of the second user, the QPSK signal should   !   !!  % % !#  ¢ QAM signal is decoded. LabVIEW Implementation of NOMA Modulator: To deploy the superposition coding in the transmitter in LabVIEW, new communication modules are introduced in gNB. In implementing PDSCH of our NOMA model, two modulation modules are used for the paired users instead of unique modulation module in the ordinary LTE system. As illustrated in Figure 10, a far user (UE1) is modulated using QPSK modulator and near user (UE2) is modulated using 16 QAM modulator. The modulated symbol from the paired users are superposed (with different power level) and mapped to the same resource element and then modulated to OFDM symbol. Figure 11 depicts the recourse element mapping block which adds the symbols of the two users into one recourse element. The output of resource element mapper is applied into OFDM modulation  $ '  X +"XX#‚! % Œƒ header, tail and training sequence are added to OFDM signal to successfully decode it at receiver side. Finally the OFDM signal passes through pulse shaping block to keep the inter symbol interference caused by the channel in control and make  !  \ # #Œ%%!  !!$$   symbols ready to send to channel. Figure 12 illustrates the constellation of the superposed signal which is similar to constellation of 64 QAM.

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Figure 10. NOMA modulation with QPSK and 16 QAM users.

Figure 11. Resource element mapper of NOMA.

Figure 12. Constellation of super position signal.

LabVIEW Implementation of NOMA Demodulator: The block diagram of receiver is shown in Figure 13. The data received by the user’s antenna is %     !   '    '  '  Œ the known waves after being contaminated by noise in channel. So it improves the signal to noise ratio (SNR) and the signal detection. OFDM synch is used to achieve symbol timing and frequency offset. Since the transmitter and receiver do not have a common time reference as the local oscillator of the transmitter and receiver could have some shifts  \ %   %!!>  '    symbol boundaries to avoid inter symbol interference. Beside symbol timing and frequency offset, OFDM synch is used to detect the start and the end of each frame (frame detection).

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OFDM channel estimation is based on minimum mean square error (MMSE) technique. Channel estimation is necessary as it removes the effect of impairments caused by frequency selective fading. Strip control block simply removes header and tail control information as training sequence and zero pads to recover the data. OFDM demodulate block demodulate the received samples. Finally, the decoder block maps the received stream of complex symbols to the corresponding binary values of each user according to the modulation used by the user. This block is developed in 5G to suit superposed NOMA signal transmitted by paired users. Far Receiver (QPSK): The processing procedures of the two UEs in the receive path are different. The far QPSK user directly decodes the superposed signal as the current OMA LTE receiver. The added signal from 16 QAM user is treated as a noise. In order to successfully decode the QPSK signal, the allocated gain to QPSK signal should be higher enough to maintain the received signal (which consists of superposed signal and estimated channel noise) still exists in the correct constellation part. Figure 14 illustrates the basic block diagram to modulate the QPSK user’s data. Near User Receiver (16 QAM): Different from the QPSK far receiver, an additional SIC process is conducted to the 16 QAM near receiver. The "; ¢*ƒ=  '  !     ' % % ! exactly as the same as the UE1 receiver extracts QPSK signal. Decoding QPSK signal for 16 QAM user is even easier than QPSK user as the user is more close to gNB than QPSK user. The QPSK signal is then subtracted from the superposed signal with same gain level it super positioned at transmitter in order to decode 16 QAM signal. So the QPSK signal should pass through the same process as in the transmit path at gNB before subtraction process. Then the UE1 signal is modulated again using QPSK modulator and cancelled from superposed signal as shown in Figure 15. The subtraction process is done by subtracting every symbol sent by the transmitter, from each symbol of QPSK far user’s symbols. The most subtraction value near zero is where the symbol of the near user is detected and this process happens on the whole 64 symbols to end up having the symbols on 16 QAM constellation of UE2.

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Figure 13. Receiver block diagram.

Figure 14. QPSK demodulator of NOMA.

EXPERIMENTAL RESULTS AND ANALYSIS The proposed 5G prototype is implemented using general purpose computers with USRP 2920. The experiments are carried out according to the proposed model explained in section II. Two users are being emulated along with a gNB. UE1 is the cell edge user and could be assigned only lower modulation scheme such as QPSK modulation while UE2 is the cell core user that is assigned higher modulation technique such as 16 QAM or 64 QAM. The two users use a downlink channel of PDSCH at the PHY layer. In these experiments, the proposed prototype operates in the FDD duplex mode and the SISO transmission mode. The DL carrier frequency is 2.66 GHz (band 7), and the system bandwidth is 5 MHz. The proposed prototype  '       %       %  ! '    ' !! '  "   phase a loopback test is deployed at each module in the transmitter and the appropriate one in the receiver. In the second phase, the overall system is tested. Both simulations using SDR and experiments using USRP are carried  !'    '!%%  In the simulation, the PDSCH of the paired users is implemented on the proposed prototype. Additive white Gaussian noise (AWGN) channel model is being used to emulate channel impairments for the receiver. Since each user is located at different position and different distances from the gNB, so they suffer from different channel conditions. Thus, they have different Eb/ N0 values at the receivers.

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Figure 15. SIC in 16 QAM demodulator of NOMA.

Firstly, we tested the constellation diagrams of the transmitter to verify that the transmitted symbols are represented in 64 symbols as shown in Figure 16. The transmitted constellation represents symbol energies of both users after being added by the superposition-coding block at the transmitter. We set the power allocation to 0.7 dB and 0.3 dB for the QPSK & 16 QAM respectively. Then we tested the constellation diagram of both users at receiver before adding the AWGN channel and proved that the concept of SIC at the 16 QAM is correctly designed. Figure 17 shows that the data is retrieved correctly for both users. After that we added the channel impairments represented by the AWGN channel block that is responsible for emulating the channel noise. We set the normalized signal-to-noise ratio (SNR) to 28 dB for both users. We evaluated the performance of the complete system by using different power !!½…   Œ%    ! % !! factor then raise it gradually. #  %  !!          %   of each user receiver. In addition, the selected modulation scheme for each user also affects the performance of the receivers. QPSK has higher performance concerning the BER but it has less data rates compared to the higher modulation schemes. gNB is responsible for deciding the modulation schemes for each user according to their channel quality. In general, it is noted that QPSK signal can be easily detected as observed from following constellations. Whereas at very low power allocation factor, the 16 QAM receiver cannot detect its data from the combined data as 16 QAM has higher BER.

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Figure 16. Transmitted constellation diagram of both users.

Figure 17. QPSK and 16 QAM constellations diagram at receivers in perfect medium.

Figure 18!!   !! *‘¢*ƒ=½ = 0.1. The QPSK user could easily detect its data from the combined signal ­  % +Ÿ½©‡‚  ! }  comparing with higher power allocation, while that 16 QAM signal cannot be detected. To be able to successfully decode 16 QAM signal, the value of the power !!½    !  !! ' " is noted from Figure 19 some sorts of improvements in constellation of 16 QAM signal and it can be detected. The channel quality plays substantial role in selecting the power allocation factor of each users. We studied the resulted BER versus Eb/N0 at

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%  % !!Figure 20 illustrates the results at very low % !!+½©‚"   ¢*ƒ=!  high BER and it cannot be detected except at very good channel situation (Eb/N0 > 35 dB). On the other hand, QPSK enjoys low BER even at lower Eb/N0. There are some sorts of improvements in the BER versus Eb/N0 graph for the 16 QAM user as the power allocation factor increases. While the BER of QPSK receiver increases with increasing of power allocation factor as shown in Figure 21 and Figure 22  ½© % ' !

Figure 18.; !!*‘¢*ƒ= ";  ½© and Eb/N0 = 28 dB.

Figure 19.; !!*‘¢*ƒ= ";  ½© and Eb/N0 = 28 dB.

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Figure 20.}'’Š½©

…  !      %    %  !!  ½ '  according to the deployed modulation scheme. The BER is improved for ¢*ƒ=   ½ ! ?  *‘…  '    ¢ *ƒ=        '     '  ½ # >  employ NOMA in a practical network, the power allocation factor and the modulation scheme for paired users should be selected in accordance with   !\!%   ! %  %   together. To get best performance for both users, it is recommended to start with lower values (where 16 QAM users cannot be detected) and increase ½ % %!! ¢*ƒ=   #    '  some additional signaling to the adaptive modulation and coding (AMC) %        §# %   X Œ%! >   Š}  distribute the power in different UEs dynamically based on their channel quality indicator (CQI). Furthermore selecting the paired users should be optimized in the overall network. We can see from the experiment that if the ¢*ƒ= ­  !>! '! ½  !   and so it can be paired with QPSK user with very bad channel situation and vice versa.

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Figure 21. BER vs Eb/N0½©

Figure 22. BER vs Eb/N0½©

Real time experiments: More development is needed for the current deployment of 5G prototype in order to run in real time. So we consider an ‰  Œ%   '!  '       !    > !  ! resources, single educational mode, unbalanced development of distance education, imperfect equipment and limited teacher skills. The tele-information transfer technologies commonly used in telemedicine include mobile communication, satellite, wireless network and so on (Feng, 2010). Satellite communication is characterized by transmission delay, easy interference and high cost. Wireless network has regional limitations, unstable transmission speed, large equipment and other disadvantages. If you have a light hardware, a software that supports live streaming, and a fast enough mobile network, all these problems will be easy to solve. 5G and concomitant emerging technologies can expect to be able to overcome these challenges. Compared with the above two communication methods, mobile communication, 5G is a representative, has more advantages. The advantages is light equipment, complete functions, high transmission speed, stable signal and so on.

5G COMMUNICATION Distance medical education relies on the scalability, transparency, fault tolerance, geographical coverage and security of mobile communication, it realize the ideal model of distance medicine education. # `^         !   !>   !! ‰     '  ! % society, also includes medical education. Although the 5G technology is driven by a bundle of characters, such as data transfer rate, latency, coverage, power, and network energy usage, however, the following unique features are most valued to healthcare: 1) high-speed data transfer rate; 2) super-low latency (delay in the data transmission-response system); 3) the connectivity and capacity; 4) high bandwidth and durability per unit area (Li, 2019; Riva & Gamberini, 2000), as Figure 2 shows. Some scholars believe that the 5G era will be the fourth industrial revolution (Steinert, 2006). It is not only a new generation of mobile information technology>!    !!% >  !          !  !!  , the Internet of Things, the blockchain and the smart economy (Panwar, 2016).

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Figure 2. The basic features of 5G technology (Riva & Gamberini, 2000).

`^       >  `^   high speed, low delay, high reliability and great connectivity, which can accelerate the development of new technologies, thus transforming traditional industries and creating new opportunities for the development of digital transformation in all walks of life. For example, THE integration of 5G with 8K, AR/VR and other technologies, it also can promote the development of applications such as UHD live streaming, 3D video, cloud games and telemedicine education (Pattichis, 2002).

PROSPECT 5G in the Distance Medical Education The network information society develops rapidly. How can medical students learn more efficiently, more excellent, more able to adapt to the social, which poses great challenges to the traditional medical education. 5th generation mobile networks (5G) is a good bridge linking the network information society and medical education (Ullah, 2019). The performance goals of 5G are high data rates, low latency, energy savings, reduced costs, increased system capacity and large-scale device connectivity. Network

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information technology has been widely used in medical field, especially in the education of basic medical knowledge and clinical skills training, application of information technology can get rid of the limitation of traditional teaching, not only can the innovation teaching idea, expand teaching space, improve the teaching quality, still can improve teaching efficiency, reduce material inputs, thus effectively promote the teaching reform and development. With the 5G, teachers can more easily talk with students face to face, impart theoretical knowledge and practical experience, conduct interaction and discussion, guide simple operational exercises, and conduct examinations and tests, even if they are thousands of miles apart. Although 5G technology is still in the initial stage of development, its unique advantages determine its broad application prospects in medical education. Due to the rapid development of 5G technology in China, commercial 5G   !  !!%%!   !>  ! ! has been explored in combination with the advantages of 5G technology. At present, domestic and foreign 5G technologies are mainly applied in the  ! !  !>  >   >      >  !!   > !   !  >  ! intelligence assisted diagnosis and treatment, etc. (Ullah, 2019; Fitts, 1967). At present, mobile communication devices and technologies are rapidly updated. Mobile communication devices, typical representatives of which are mobile phones and tablets, can be combined the advantages of 5G with APP software which have the live broadcast function, traditional medical education mode can be broken and new medical distance education mode can be explored. Solve the problems existing in traditional medical education.

Application of 5G in Clinical Practice At present, most medical students in internship generally report that most medical schools are short of substitute teachers, uneven distribution of quality teaching resources or insufficient, etc. For example, a teacher takes more than one or even more than a dozen students, so the room in the ward and the sight of teaching is limited. The number of patients and typical diseases encountered by the teachers in the remote and underdeveloped areas is small and the equipment is limited. In areas where teachers are insufficient, students can sit in the classroom and open the studio set up by the teacher with their mobile phones. The teacher only needs to hold the mobile phone while making ward rounds and broadcast live. The perspective is more

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comprehensive and intuitive, so as to reduce overcrowding in the ward and avoid affecting the ward environment. If patients and diseases in remote and underdeveloped areas are limited and the equipment is backward, they can cooperate with medical colleges and affiliated hospitals in coastal areas and other medical developed areas to hire highly educated and experienced professors to teach, and even if they are far away from home, they can teach on-site. This can be achieved only with a mobile phone and a 5G network. In this way, students can learn more kinds of diseases and learn more advanced medical equipment even in remote areas. Students will encounter many clinical problems in clinical practice. In  %>   ! "    ! '$ after work to solve problems. If they had 5G network speed and mobile phones and software supporting the network, they would immediately consult relevant literatures to solve problems. Obviously improve students’ !        '  >%' !   > and improve the quality of education. Most of the surgical interns lack the relevant training to enter the operating room and work as an assistant on the operating table, or some hospitals have limited training but are relatively abstract, so they are not familiar with the relevant operation and are prone to make mistakes. ;   ! %! Œ%  >   the Fitts-Posner 3-stage theory of motor-skill acquisition [24]. If the teacher can demonstrate the relevant operation of the operating room in the way of live broadcast by mobile phone, it will be more intuitive and easier for students to master and avoid mistakes. %   >    ;{‹"„ È +‡‡ ‚ ‡ `  %’’  '’%! ! ’  ’ ! ! ’ ’¢ ’’    ! 12. Jing, H. Q. (2009). The Development of Distance Medical Education in China. Continuing Medical Education in China, 4, 11-14.

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13. Kirkpatrick, D. L. (1994). Evaluating Training Programs: The Four Levels. San Francisco, CA: Berrett-Koehler Publishers. 14. Lenthall, S. (2011). Nursing Workforce in Very Remote Australia, Characteristics and Key Issues. Australian Journal of Rural Health, 19, 32-37. https://doi.org/10.1111/j.1440-1584.2010.01174.x 15. Li, D. (2019). 5G and Intelligence Medicine—How the Next Generation of Wireless Technology Will Reconstruct Healthcare? Precision Clinical Medicine, 2, 205-208. https://doi.org/10.1093/pcmedi/pbz020 16. Li, L. (2004). China’s Higher Education Reform 1998-2003: A  ƒ     ' > `> ˜ ! %     !   >  !  and information services has gradually deepened, and there has been a phenomenon of intersection and integration between industries, which can be divided into internal integration and external integration [8]. Logistics is a fusion of distribution, transportation, warehousing and other industries compound service industry [9], in order to meet the demand of “one-stop” logistics services, different functions of the logistics enterprises between strategy and cooperation continued to deepen the reform, aiming at expansion capacity to conduct horizontal integration development, and gradually formed at the beginning of the traditional logistics industry, which is logistics industry of internal integration.The external integration of logistics industry is also known as the cross-industry integration, producing a new industrial that is different from the original industry and providing additional functions and stronger competitiveness for the logistics industry [10]. With the integration of logistics industry and other industries, more and more new logistics industries keep emerging. Cold-chain logistics, agricultural logistics parks are all the products of the integration betwen logistics industry and the primary industry. In addition, the integration of logistics industry and manufacturing industry improves the industrial chain and value chain of enterprises. Besides, the logistics industry and tertiary  ' !   > §   %   ! ! Thanks to the leapfrog development of logistics industry in our country, Chinese logistics industry has gradually integrated with the three major industries, greatly enriching the additional functions of the logistics industry. §   !  >'!  %  !  !>%' % ' '  ! institutions    }$   ‡‡‡>  È        '   ; >; ?!  business has achieved a breakthrough from scratch [11]. During this period, logistics and technical means were gradually integrated. The application of rf scanning code technology and IoT made the logistics process visible, traceable and controllable, so as to improve the problem of information asymmetry between Banks and enterprises and reduce the loan risk of Banks or other

!ƒ%  >     `^  “  ! format, and realizes the transition and upgrading of the logistics industry again.

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ANALYSIS ON THE INNOVATION PATH OF CHINA’S LOGISTICS FORMAT IN THE 5G ERA Nowadays, although the traditional logistics system has made remarkable achievements, with the rapid economic development, the logistics industry can no longer meet the needs of economic development, and the logistics industry innovation is extremely urgent. As an inseparable important part of the 5G industrial chain, logistics will undergo great changes due to the generation of 5G. Therefore, the value of 5G for logistics is self-evident [12]. The reason why 5G can be widely applied in the logistics industry is that there is a close connection between logistics and the IoT. 5G has three features: ultra-low delay, high-speed broadband and mass access. Each of these features is likely to bring leapfrog   !!%  ! !!  >"   >  '  !>  !!%    ! Œ! the key to the development of smart logistics [13]. Therefore, it is possible to realize the intellectualization of logistics transportation, the automation of logistics warehousing and the networking of logistics information. In >  `^!$ %%!   ! !  , providing real-time data feedback and prevent tampering, so as to improve the reliability of logistics supervision [14]. 1)

Establishing an intelligent logistics traceability system based on 5G Smart logistics tracing system is the use of the IoT and Internet to realize tracking and traceability of products, Suppliers can conduct controllable query on the forward logistics of goods and report analysis, the consumers can reverse query information through the platform or software after receipt of the goods. It usually used in agricultural and sideline products or cold-chain logistics system [15]. Its essence is a bottom-up, multi-level, distributed and multi-node information sharing chain formed by relying on the characteristics of 5G+ IoT massive links. The smart logistics traceability system is divided into four layers. A large number of intelligent data readers in the perception layer capture the relevant data for analysis and storage, and then transmit to the upper layer for application scenarios. 5G communication technology serves as the data circulation medium of this information sharing chain. The intelligent logistics traceability system uses a large number of connected devices of perception layer for data recording by

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2)

3)

using 5G’s mass connection feature, and edge server using 5G network for data collection, information gathered the data center. The application layer draws the visual scene through these data, providing accurate decision for logistics management. Implementing fully automated logistics transportation based on 5G The establishment of the fully automated logistics transportation system depends on the multi-function unmanned intelligent robot. Therefore, the development of unmanned driving technology and unmanned distribution system greatly limits the development of the fully automated logistics industry. In recent years, unmanned driving technology has been a hot topic in various industries, but it have not been a major breakthrough due to its high technical threshold, legal constraints and other issues. With the appearing of 5G, the characteristics of high bandwidth, low latency and wide connection not only make the unmanned driving industry more “hot”, but also provide a feasible solution from the technical level, making full automatic logistics transportation no longer just “a paper idea”. Compared with the traditional transportation, the fully automatic logistics transportation is controlled by the computer. The terminal equipment is connected to the network through the Internet of vehicles technology, and the data obtained by the control center is used to make the path decision, so as to realize the complete interaction between people, vehicles and roads, making the ! %        ƒ   driving technology needs the support of Internet of vehicles, and powerful data communication technology is the cornerstone of Internet of vehicles technology. The development of 4G to 5G not only improves the speed of data transmission, but also effectively solves the problem of “Shared sensing” between vehicles, greatly avoiding accidents and enhancing the safety and reliability of transportation. Maintaining logistics security by 5G+ blockchain Traditional logistics systems are generally based on large-scale and scalable mass data storage technology, which requires the analysis and safe storage of multi-party data [15]. In recent years, logistics security has become the focus of the industry due to the

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frequent occurrence of security problems such as lost bag and wrong collar or information leakage. Therefore, the exchange of logistics data and information based on block chain technology emerges at the historic moment, realizing the safe transmission of physical information. Due to the characteristics of block chain technology such as distribution and sharing mechanism, the recorded items have a strong traceability and can realize the capitalization of commodities. Therefore, block chain technology can be widely applied in the

!! `^> >  foundation of block chain technology, ensuring the real-time and

    % . ">   <  !!  ‰   real trade situation of small and medium-sized enterprises, ensure    ‰     > !“   credit penetration of core enterprises in the supply chain. The ­     !    !% ! institutions effectively evaluate credit risks, reduce the default  ! !% , and maintain the security of the whole logistics process.

CONCLUSIONS Smart logistics tracing system is the use of the 5G+ IoT and Internet to realize tracking and traceability of products, which implemented the data flow of information chain; 5G also provides a feasible solution for unmanned driving technology because of the characteristics of high bandwidth, low latency and wide connection. In addition, with the integration between logistics industry and other industries, the security of logistics has become increasingly important. So the exchange of logistics data and information based on block chain technology emerges at the historic moment. All of them are the products of 5G and the development of smart logistics. In a word, 2019 is a year of rapid rise of 5G. The emergence of 5G is   !! !$  !  § >     ! ' !   '  industry, must be prepared to lead the popularization of 5G. With the rapid growth of the logistics industry, the traditional logistics industry can no longer meet the diverse needs of people. The emergence of 5G communication technology is bound to promote the innovation of the traditional logistics industry.

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In recent years, with the rise of technological means such as IoT+, big data and cloud computing, scientific and technological innovation has triggered a new round of logistics competition, and cross-border integration ability has become the core competitiveness of new logistics industry. The birth of 5G not only optimizes the communication technology in the logistics system, but also serves as the technical support and media for many hot technologies to help them to deeply integrate with the logistics industry. At the same time, 5G supports blockchain to truly achieve real-time data capturing and tracking, making blockchain “better”. The massive connectivity of 5G makes the Internet of everything possible. The flexibility of using 5G technology is improved by the on-demand networking, which enables the IoT to be closely integrated with the logistics industry. These things together make it possible to be monitored at any time and tracked everywhere. In the future, 5G will definitely promote the split development of the logistics industry and eventually form the 5G ecosystem.

NOTES 1

China Federation of Logistics and Purchasing.

2

http://www.Chinabaogao.com.

3

Ai Media Consulting.

     The authors declare no conflicts of interest.

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REFERENCES 1. 2.

3. 4.

5.

6. 7.

8. 9. 10. 11.

12.

13. 14.

Zhang, C. and Peng, D. (2013) Countermeasures on How to Develop China’s Smart Logistics. China Business and Market, No. 10, 35-39. Zhang, H. (2011) Study on the Basic Connotation and Implementation Framework of Intelligent Logistics. Market Modernization, No. 23, 44-46. Shi, Y. (2011) Intelligent Logistics Built on the Basis of the Internet of Things. Logistics Technology, No. 17, 44-49. Shi, R. (2016) The Information Platform Construction of Smart Logistics Parks Based on Big Data. Enterprise Economy, No. 3, 134138. Tao, J. and Xu, Q. (2013) Research on the Business Type Structure and Upgrade Path of China’s Logistic Development. China Business and Market, No. 7, 23-27. Li, F. (2006) Study on the Roadmap of Retail Format Innovation. Studies in Science of Science, No. S2, 654-660. Weng, X. (2017) Some Reconsideration on the Characteristics and Innovative Development of China’s Logistics Industry. China Business and Market, No. 3, 8-17. Li, M. (2011) Study on the Mechanism of Logistic Industrial Convergence. Ph.D. Thesis, Changan University, Xi’an. Li, X. (2010) Discussing in the Main Problems Existing in the Logistics Industry in China. China Journal of Commerce, No. 18, 68-69. Qi, B. (2007) Logistics Industry: Convergence and Organizational Innovation. Ph.D. Thesis, Fujian Normal University, Fuzhou. Li, Y., Wang, S. and Feng, G. (2010) Practical Development and Theoretic Review of Logistics Finance—A New Discipline Direction. Systems Engineering-Theory & Practice, No. 1, 1-13. I-Yiou (2019) 5G Will Reshape the Format of Logistics Development, with DEPPON, G7, CAINIAO and JD Becoming Pioneers. https:// baijiahao.baidu.com/s?id=1624040053207746301&wfr=spider&for= pc Xi, Y. (2019) 5G Gives Wings to the Development of Smart Logistics. China Logistics & Purchasing, No. 16, 27-28. Hu, W. and Qin, M. (2018) Blockchain Technology Brings Changes to Logistics Finance. Chinese & Foreign Entrepreneurs, No. 5, 59.

CHAPTER 12

Limiting Energy Consumption by Decreasing Packets Retransmissions in 5G Network

 Apiecionek1 1 Institute of Technology, Kazimierz Wielki University, Bydgoszcz, Poland

ABSTRACT This article presents the potential of using Multipath Transmission Control Protocol for limiting the energy consumption in 5G network. The number of errors occurring during packet transmissions and in effect the number of retransmissions affect the consumption of energy by the devices in the network. The paper analyzes the potential energy savings from implementing an algorithm for detecting problems and predicting the future retransmissions. Although this is the main object of the paper, it must be

Citation Î$“ƒ%  $>ϧ   ;%  „   $  Retransmissions in 5G Network”, Mobile Information Systems, vol. 2017, Article ID4291091, 9 pages,2017. https://doi.org/10.1155/2017/4291091. Copyright: © 2017by Authors. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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emphasized that the proposed method also allows increasing the speed of transmission and improving the security of the data and it is easy to implement in 5G networks.

INTRODUCTION A general opinion on the 5G network is that this technology could not be used in the near future, mostly due to the fact that the business segment remains unprepared for its implementation. Lots of GSM/3G/LTE operators have invested much in their infrastructure and what they now focus on is the return on this investment. In a technical aspect> `^   $         !!  parameters [1, 2]:(i)1 millisecond end-to-end round trip delay (latency). (ii)1000x bandwidth per unit area.(iii)10–100x number of connected devices. (iv)(Perception of) 99.999% availability.(v)(Perception of) 100% coverage. (vi)90% reduction in network energy usage.(vii)Up to ten-year battery life for low power, machine-type devices. Only a network meeting these provisions can be called a 5G network. An additional parameter is a new radio interface [1, 3–5]. The ability to work with more than one radio transmitter/receiver, called MIMO (multipleinput, multiple-output) [1–3, 6, 7], is also very important. Moreover, it is often postulated that 5G network should be able to coexist with the old network. It should use Ethernet protocols and treat the old network as a backup [1]. It is essential for critical infrastructures, where the reliability of the network is a top priority [8]. One of the existing technologies which can be taken into account for cross-layer optimization for 5G network communications and which is able to use MIMO as well as the existing infrastructure, is MultiPath Transmission Control Protocol (MPTCP in short). MPTCP technology allows using all the existing links to provide one stable and fast connection between two points of communication. The fact of using more than one connection in 5G network is something which does not neglect the MPTCP technology but can lead to limiting the energy consumption, which can be achieved by reducing the amount of retransmissions. No less important than the reliability of 5G network is its speed. In order to eliminate the problems with delays in MPTCP, fuzzy logic can be applied,

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especially Ordered Fuzzy Numbers (OFN in short, called in some papers Kosinski’s Fuzzy Numbers), for predicting problems in the network [9, 10]. This will allow predicting errors in the network and deciding on using a different link. Current State of Research. MPTCP is not widely used today. The best known user of this technology is Apple company, which has implemented it in its IPad and IPhone devices. Most research on this solution focuses on the ability to maintain a stable connection between two points by the use of different links [11, 12], while the issues concerning Ordered Fuzzy Numbers are mostly investigated by the coworkers of professor Kosínski [9, 10]. However, by far no research has been performed on the usability of OFN as a means for predicting the increase in bit error rate in TCP (Transmission Control Protocol) transmission, which could allow for decreasing the number of retransmissions of packets. This paper aims to analyze this new practical application of OFN solution combined with MPTCP technology for limiting the consumption of energy during the transmission of data. The following section of the paper describes MPTCP technology in regard to its features and implementation status. Section 3 describes OFN and their applicability in 5G network along with the MPTCP technology, for the purpose of limiting the energy consumption. The recapitulation of the %% ' ! ! $

MULTIPATH TCP Prior to presenting the MPTCP technology, it is necessary to first introduce the concept of TCP [13], which is used for transferring data between the processes running on different machines. TCP can send data in two directions between two hosts. A unique identifier of the TCP connection is two pairs of values (one for each side of the connection)—IP and port number. By using checksums and sequence numbers, TCP provides a complete and orderly data exchange for higher layer applications. The header presented in Figure 1 contains all necessary details to establish a connection.

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Figure 1. TCP header.

Before the application starts transmitting the information, it is necessary to exchange the initialization data, as presented in Figure 2. Host A sends a      «Š‰>  }   ' %$   $«Šƒ;‘‰X!!> ƒ  %  >  !ƒ;‘‰”•

Figure 2. Three-way handshake.

TCP connections cannot move from one IP address to another. When a PC switches from Ethernet to Wi-Fi, it is assigned a different IP address. Thus, all the existing TCP connections must be shut down and reestablished. =#;  Œ  %  #;  !!  the client to establish multiple connections using different network cards          #   ! !!  {   X““ Š , and it allows linking the change of trend to a fuzzy number. #    %         %   ““     $   ! ‡‡  ‘Ò$  Ó“ ”‡• X     ‘Ò$> %!   %    $% “  Ô!Փ$, led to the introduction of the Ordered Fuzzy Numbers model, OFN [30–32], as follows. „ ƒ  ““  ‫ ܣ‬is an ordered pair of functions (1) where ‫ݔ‬up, ‫ݔ‬down”>•Öܴ are continuous functions. Respective parts of the functions are called part up and down and are presented in Figure 9. Figure 10 presents the OFN in a way referring to fuzzy numbers.

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Figure 9. Ordered Fuzzy Number—function up and down.

Figure 10. Ordered Fuzzy Number presented in a way referring to fuzzy numbers.

The continuity of the two parts called UP and DOWN shows that they  ! %   #       !!  values:

(2) If both functions within the fuzzy number are continuous, then their     !š„{…Š inverse functions On this assumption the following equations are valid:

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(3) , then If a constant function equal to 1 is added within the interval the result is functions UP and DOWN in one range (Figure 9, where ߤdown = ‫ݔ‬down and ߤup = ‫ݔ‬up), which may be treated as a carrier. Thereby, the function of membership ߤ(‫ ܴ   ““ ‚ݔ‬set by the following formulas:

(4) # ““        \ ! parameter, order, whereas the following interval is the carrier: (5) The limit values for UP and DOWN functions are

(6) In general, it can be assumed that Ordered Fuzzy Numbers take a form % “ {XŠ    !'!  (7) Figure 11 presents a sample of Ordered Fuzzy Numbers, including their characteristic points in a positive and negative order.

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Figure 11. Fuzzy number in OFN notation where the order is (a) positive or (b) negative.

Functions ݂‫ܣ‬, ݃‫ ܣ‬correspond to parts up‫ ܣ‬and down‫ܴ ك ܣ‬2 , respectively, which gives

(8) Orientation corresponds to the order in graphs ݂‫ ܣ‬and ݃‫ܣ‬ „ ƒ   %  ““  ‫ ܣ‬is the function ߤ : ܴÖ”>•  ‫ ܴאݔ‬as follows:

(9) #        %     %%!     ! rules in a similar way in classic fuzzy numbers. All quantities that can be found in fuzzy control describe a selected part of the reality. The process of determining this value is called fuzzy observation.

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Definition 3. Reversal of the orientation of the ordered fuzzy number ‫ ܣ‬is the replacement of the part up (function ݂‫ )ܣ‬with part down (function ݃‫)ܣ‬. This operation is described as follows: (10) where ‫ ܣ‬means an ordered fuzzy number   % (݂‫ܣ‬, ݃‫)ܣ‬, ‫ ܤ‬is a result of the operation of reversal of the OFN orientation, ·žŸ¸! '   {XŠ#   obtained in that way is called a reversed OFN or a reversed orientation number. One of the most remarkable notations interpreting ordered fuzzy number is a set of key points: (11) Basic arithmetic operations, where ordered fuzzy number ‫݂( = ܣ‬, ݃)is %   and ‫ ‚ >݁( = ܤ‬%  > relate to the following formulas: (i)

addition: ‫ ܣ‬+ ‫ ݂( = ܤ‬+ ݁, ݃ + h) = ‫ܥ‬,

(12) (ii)

scalar multiplication: ‫݂ߣ( = ܣߣ =ܥ‬, ߣ݃), (13)

(iii)

subtraction: ‫݁Ÿ݂( = ܤŸܣ‬, ݃Ÿ ‚©‫ܥ‬,

(14) (iv)

multiplication: ‫݁ × ݂( = ܤ × ܣ‬, ݃ × h) = ‫ܥ‬,

(15) As it was presented in Section 2, the MPTCP technology could be used for providing better network security in relation to such parameters as the destination reachability and network reliability. Of course, the transmission in

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all channels may generate some errors which require packet retransmission. This data retransmission will be made over the same channel in which the error occurred.When the number of retransmissions rises, the energy consumption is increasing as well. The application of OFN could speed up the decision to change the data transmission path, which allows decreasing the number of transmission errors. OFN allow predicting data loss in the currently used channel and can also help make a decision to retransmit the packet faster and limit the number of retransmissions as well. Lowering the number of retransmissions allows limiting the energy consumption.

OFN FOR ERRORS PREDICTION In this section the algorithm which uses OFN for detecting potential problems is described. The algorithm measures a TCP retransmission in all the used channels during the transmission and provides it in a percentage value of the transmitted packets. This measure is made both in a given period of time—a timeslot—and continuously. Four timeslots of the continuous measure could be defined in time: (16) where ‫ ݅ݐ‬is a current timeslot. These four results together give a fuzzy number in OFN notation, where

This fuzzy number in OFN notation is presented in Figure 12.

Figure 12: Fuzzy number in OFN notation.

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The definition of fuzzy observation of the connection used is as follows. Definition 4. Fuzzy observation of ‫ ܥ‬connection in time ‫ ݅ݐ‬is a set (17) where (18) timeslot of the measurement (19) This provides Lemma 5 Lemma 5. One has

(20) in another situation ‫݅ݐܽ݃݁݊ܥ‬V݁. ƒ  >    !$ during the connection observation should give (i) positive order of OFN when the packet retransmission count increases, (ii) negative order of OFN when the packet retransmission count decreases. The four measurements performed during the data transfer allow preparing fuzzy number in OFN # ' ““ ' =#; >   as follows. „ ¢X““ ' =#;    by the following formula:

(21) where ‫݅ݓ{ א ݅ݓ‬,...,‫ }݊ݓ‬describes the impact on all connections. ƒ„ >%!    =#; ! ing OFN.

The Algorithm of OFN Scheduler The algorithm with application of OFN for predicting transmission errors in MPTCP transmission consists of four steps.

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Step 1. The administrator declares the start value for ‫ ݅ݓ‬and ‫ ݅ܮ‬for all MPTCP links, where ‫ ݅ݓ‬describes the impact of errors on all connections and ‫ ݅ܮ‬describes the load of the whole data which should be sent by the ݅ connections when the transmission starts. ‫ ݅ܮ‬is expressed in percentage rate. Step 2. The amount of packets ܲ݅ which will be transferred over each connection during each timeslot is calculated using the following formula: (22) Step 3. During the transmission the fuzzy observation of connection ‫ ݅ܥ‬is calculated for each connection according to data retransmissions and fuzzy observation of MPTCP ܵ݅ is calculated according to the given definition. Step 4. When the calculated ܵ݅ is positive and it is higher than the acceptance level AL, it means that the number of errors is increasing on this connection. In this situation ‫ ݅ܮ‬for the given connection will be changed according to the following formula:

(23) When the calculated ܵ݅ is negative, the number of errors is decreasing on this connection. In this situation the ‫ ݅ܮ‬for given connection will be changed according to the following formula: (24) The ErrorCorector is a value which describes how fast the system should stop using the connection in which the amount of errors increases. This value should be also declared by the network administrator.

Simulation Test Results In order to check a MPTCP scheduler with OFN, a simulation was performed. The system was designed with two connection links: Connections 1 and 2, labeled ‫ܥ‬1 and ‫ܥ‬2, with maximum speed 100 Gbit/s. The parameters of the algorithm were declared as follows: (i) the corrector for the links ErrorCorector = 2; (ii) acceptance level AL = 3; (iii) load balance on start for connection ‫ܥ‬1 was ‫ܮ‬1 = 66; (iv) load balance on start for connection ‫ܥ‬2 was ‫ܮ‬2 = 34; (v) the timeslots used were 60 seconds.

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Table 3 presents the results of the used algorithm dependently on the number of errors during the data transfer. The fuzzy observation of the link gives OFN numbers ܵ1 and ܵ2. Table 3: Fuzzy observation of the connections.

As it can be observed, after the 4th measure the OFN numbers is generated and ܵ1 exceeds the acceptance level, so OFN scheduler algorithm changes the load balance for the transmission, as it is presented in Table 4. The total number of packets passed to the connections changes during the timeslots according to the load balance set at the beginning of the test. Table 4: Load balance for the connections.

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This affects the number of packets transferred through the connections and, depending on the error level on the link, provides different number of errors. In the simulation there were 40000 packets passed to the link. Table 5 presents the numbers of errors in the connections with and without OFN scheduler in MPTCP. As it can be noticed, the total number of errors without OFN scheduler is higher than in the variant using this solution. Table 5: Number of errors in packet transmission.

The Evidence for an Effect on Energy Consumption The main goal of this paper is to analyze the effect of the proposed solution on the energy consumption. As it can be noticed, the system of transmission itself consumes much power. For instance, sending data packets over radio network in smartphones drains their batteries, while nowadays there are many processors available described as Low Energy Usage. The presented algorithm does not require any complex calculations,           %  %      > the problem is the energy consumption by the system of transmission. The process of sending data is power-consuming not only for the sender device, but also for the entire IT system. This system in the case of 5G networks comprises numerous devices: transmitters, switchers, and routers. Each of these devices on the packet’s path uses energy. In a case where the packet is retransmitted the total energy consumption is even higher as(i) the receiver has to realize that a problem with the received data occurred and, for example, recalculate the CRC checksum;(ii)the receiver has to send the information to the sender about the problem with transmission;(iii)the sender has to send the data again.

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As it was described above, in the case of retransmission, the data is sent twice, and, moreover, additional data concerning the problem is transmitted over the network, which means that the system is used thrice. The analysis of the proposed algorithm allows concluding that implementing simple calculations and making optimal decisions by the         %     !  IT ecosystem.

CONCLUSIONS In this article, a special algorithm for mobile connections, applicable in 5G network, was introduced. For this algorithm a MPTCP concept was used and the energy consumption as well as the security aspect were developed by a special usage of fuzzy observation of the errors in the transmissions. The presented new concept of MPTCP scheduler combined with OFN was tested during a data transmission simulation. As it was presented in the previous section, the proposed algorithm allows reducing the number of retransmissions, as less packets are transferred over the connection link in which some problems are detected. Such application of MPTCP allows limiting the energy consumption by decreasing the amount of data which has to be transmitted. This is a possible usage of OFN for improving the existing solutions like MPTCP in a simple way, without any complicated algorithm requiring a high level processor performance. This is very important for the 5G network, in which the speed and power consumption is a given parameter. ƒ    >      in which not only 5G, but also the existing infrastructure will be used. In this situation there will be more than one connection to the same destination. For this reason, the MPTCP with OFN could give a valuable solution in critical network connections. The presented algorithm is able to provide a better energy management and higher transmission security and reliability without generating higher costs in the implementation process [33–35].

Competing Interests #   !    ‰    %!cation of this paper.

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REFERENCES 1. 2. 3. 4. 5. 6. 7. 8. 9.

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Understanding 5G: Perspectives on future technological advancements in mobile, December 2014, https://www.gsmaintelligence.com/. 5G radio access, Ericsson White paper, Uen 284 23-3204 Rev C, April 2016. View on 5G Architecture, 5G PPP Architecture Working Group, https://5g-ppp.eu/white-papers/. 5G PPP Pre-Standardisation Working Group, Lisbon, Portugal, October 2015, https://5g-ppp.eu. M. Maternia and S. E. El Ayoubi, Eds., 5G PPP Use Cases and Performance Evaluation Models, version 1, 2015, https://5g-ppp.eu/. 5G and Energy, 5G PPP Architecture Working Group, https://5g-ppp. eu/white-papers/. 5G Automotive Vision, “5G PPP Architecture Working Group,” 2015, https://5g-ppp.eu/white-papers/. 5G and e-Health, 5G-Infrastructure-Association, September 2015, https://5g-ppp.eu/. J. Czerniak, “Evolutionary approach to data discretization for rough sets theory,” Fundamenta Informaticae, vol. 92, no. 1-2, pp. 43–61, 2009. | MathSciNet …‘Ø$>$% “>ƒ>·„ ““ ! of ordered fuzzy numbers,” IEEE Transactions on Fuzzy Systems, vol. 21, no. 6, pp. 1163–1169, 2013. C. Raiciu, C. Paasch, S. Barre et al., “How hard can it be? Designing and implementing a deployable multipath TCP,” in Proceedings of the Usenix Symposium on Networked Systems Design and Implementation, San Jose, Calif, USA, April 2012. C. Paasch, G. Detal, F. Duchene, C. Raiciu, and O. Bonaventure, “Exploring mobile/WiFi handover with multipath TCP,” in Proceedings of the ACM SIGCOMM Workshop on Cellular Networks: Operations, Challenges, and Future Design (CellNet ‘12), pp. 31–36, Helsinki, Finland, August 2012. J. Postel, “Transmission control protocol,” RFC 793, IETF, 1981. A. Ford, C. Raiciu, and M. Handley, “TCP extensions for multipath operation with multiple addresses,” RFC 6824, 2013.

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15. U. Krieger, Evolution of Transport Protocols in high-Speed Networks, Lectures materials Multimedia-Kommunikation in Hochgeschwindigkeitsnetzen (KTR-MMK-M), 2014. 16. K. M. Schneider, K. Mast, and U. R. Krieger, Adapting Content-Centric Networking to the Characteristics of Multihomed Mobile Terminals, 2014. 17. Î Apiecionek and M. Romantowski, “Secure IP network model,” Computational Method in Science and Technology, vol. 19, no. 4, pp. 209–213, 2013. 18. L. Apiecionek and M. Romantowski, “Security solution for cloud computing,” Journal of Information, Control and Management Systems, vol. 11, no. 3, 2013. 19. L. Vokorokos, M. Ennert, M. Hartinger, and J. Radušovský, “A survey of parallel intrusion detection on graphical processors,” in Proceedings               !", Spišská Nová Ves, Slovakia, November 2013. 20. W. R. Cheswick and S. M. Bellovin, Firewalls and Internet Security: Repelling the Wily Hacker, Addison-Wesley, 1994. 21. B. Chapman and E. D. Zwicky, Building Internet Firewalls, O’Reilly & Associates, Inc., 1995. 22. ÎApiecionek, M. Sobczak, W. Makowski, and T. Vince, “Multi Path Transmission Control Protocols as a security solution,” in Proceedings   !  ###           $ % (INFORMATICS ‘15), pp. 27–31, Poprad, Slovakia, November 2015. 23. L. A. Zadeh, “Fuzzy sets,” Information and Computation, vol. 8, no. 3, pp. 338–353, 1965. 24. J. Î$ “>·{! Ú­ Û ­>¸ &' ** , vol. 270, no. 5, 1988. 25. D. Dubois and H. Prade, “Operations on fuzzy numbers,” International Journal of Systems Science, vol. 9, no. 6, pp. 613–626, 1978. | MathSciNet 26. D. Dubois and H. Prade, “Fuzzy elements in a fuzzy set,” in Proceedings of the 4th International Conference on Inertial Fusion Sciences (IFSA ‘05), vol. 5, pp. 55–60, September 2005. 27. D. Dubois and H. Prade, “Gradual elements in a fuzzy set,” Soft Computing, vol. 12, no. 2, pp. 165–175, 2008.

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28. … ‘Ò$> ·{ ““   calculus,” International Journal of Applied Mathematics and Computer Science, vol. 16, no. 1, pp. 51–57, 2006. 29. … ‘Ò$   Ó“> ·X““      \  %  with algebraic operations,” Bulletin of the Polish Academy of Sciences: Technical Sciences, vol. 41, pp. 285–295, 1993. 30. …‘Ò$>$% “>„Ô!Փ$, “On algebraic operations on fuzzy numbers,” in Intelligent Information Processing and Web Mining, pp. 353–362, Springer, 2003. 31. … ‘Ò$>  $% “>  „ Ô!Փ$, “Ordered fuzzy numbers,” Polish Academy of Sciences. Bulletin. Mathematics, vol. 51, no. 3, pp. 329–341, 2003. 32. … ‘Ò$>  $% “>  ‘ X  > On Algebra of Ordered Fuzzy Numbers, Soft Computing Foundations and Theoretical Aspects, Edited by J. K. Krassimir T. Atanassow, Olgierd Hryniewicz, EXIT, 2004. 33. # ƒ$> Î  $> = ; Û>   ‘“$> ·%! and comparison of network anomaly detection based on long-memory statistical models,” Logic Journal of IGPL, vol. 24, no. 6, pp. 944–956, 2016. 34. Î  $> # ƒ$>  ‘“$>  = ; Û> ·„…#< based anomaly detection method for cyber security of wireless sensor networks,” Security and Communication Networks, vol. 9, no. 15, pp. 2911–2922, 2016. 35. J. Byrski and W. Byrski, “A double window state observer for detection and isolation of abrupt changes in parameters,” International Journal of Applied Mathematics and Computer Science, vol. 26, no. 3, 2016.

CHAPTER 13

Effective Packet Number for 5G IM WeChat Application at Early Stage Traffic Classification

Muhammad Shafiq and Xiangzhan Yu

School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China

ABSTRACT Accurate network traffic classification at early stage is very important for 5G network applications. During the last few years, researchers endeavored hard to propose effective machine learning model for classification of Internet traffic applications at early stage with few packets. Nevertheless, this essential problem still needs to be studied profoundly to find out effective packet number as well as effective machine learning (ML) model. In this paper, we tried to solve the above-mentioned problem. For this purpose, five Internet

Citation =    \> –“  «> · '  $  Š   `^ "= … ;  ƒ%%!  !   #  ;! ¸> =!  "formation Systems, vol. 2017, Article ID 3146868, 22 pages, 2017. https://doi. org/10.1155/2017/3146868. Copyright: © 2017 by Authors. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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traffic datasets are utilized. Initially, we extract packet size of 20 packets and then mutual information analysis is carried out to find out the mutual information of each packet on n flow type. Thereafter, we execute 10 wellknown machine learning algorithms using crossover classification method. Two statistical analysis tests, Friedman and Wilcoxon pairwise tests, are applied for the experimental results. Moreover, we also apply the statistical tests for classifiers to find out effective ML classifier. Our experimental results show that 13–19 packets are the effective packet numbers for 5G IM WeChat application at early stage network traffic classification. We also find out effective ML classifier, where Random Forest ML classifier is effective classifier at early stage Internet traffic classification.

INTRODUCTION During the last few years, early stage Internet traffic classification received a lot of importance in the area of network traffic classification, from the perspective of features extraction technique, mostly researcher’s proposed machine learning models, which were based on features extraction on a whole network flow in [1–3]. In 2005, Moore et al. in [4] presented a feature extraction method which is followed by many researchers for features extraction for their research. They used the whole flow traffic and extracted 248 statistical features, such as the packet sizes and maximum, minimum, and average statistical feature values. Machine learning classifiers can get very effective performance results using these statistical features [5]. These features extraction methods also showed high performance results in the identification of anomaly detection [6]. However, these feature extraction methods are not very effective in reality. Thus it is very important to classify Internet traffic at early stage keeping in view of the security policies and quality of service (QoS) management. In 2012 [7], Internet traffic classification with few packets has become a very hot topic in the area of network traffic classification. Thus for the problem of accuracy at their early stage Internet traffic classification Qu et al. in 2012 [8] studied that it is possible to classify Internet traffic at their early stage with effective accuracy performance. However, no study has been carried out on 5G WeChat application at its !    "' %%!     many packets are most effective for 5G WeChat application at its early stage  ! ܃ $     '        !  %!  !  >     

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concerned with instant messaging (IM)… ;  ! "  >         '  %$  !!  '   ! ! … ;  !  at its early stage using empirical study and information analysisX'      !!!!  !    !  !                    packets. At the end, two different statistical analyses are executed on the Œ%  ! !    ' %$   and ML !  The rest of the paper is organized as follows: Section 2 demonstrates related work. The datasets used in this study are discussed in Section 3 and Section 4 includes methodology frame work and detailed steps used in this study work. Then the introductory information of mutual information analysis>  !      !  ! >  !   theory information are discussed with details in Section 5. Results and analysis are depicted in Section 6, while we have also some discussion discussed in Section 7. In the end, conclusion is presented in Section 8.

RELATED WORK Recently, some studies have been proposed to classify Internet traffic at its early stage with few packets [7], but it is very hard to classify Internet traffic with few packets at its early stage traffic. The main problem in early stage Internet traffic classification is the extraction of effective features. Bernaille et al. in 2006 [9] proposed an early stage Internet traffic classification technique using the size of few early packets of TCP flow as features and executing K-means clustering technique utilizing 10 types of application traffic; they got very effective classification results. Huang et al. in 2008 [10] studied the characteristics of early stage Internet application traffic classification. They used these characteristics for early stage Internet traffic classification. Moreover, in 2013 the authors in [11] extracted features of early stage traffic applications. Using machine learning classifiers, they used packet size, interpacket time, average and standard deviation values, packet size, and interpacket time for early stage Internet application traffic classification. Using these features, they got very high performance results

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for early stage Internet traffic classification. Este et al. in 2009 [12] studied the features of few packets of early stage traffic and found that these features, packet size, packet interarrival time, and packet direction of early traffic, carry enough information. They also found that these features are most effective features for early stage Internet traffic classification. Hullar et al. present an automatic machine learning (ML) method for P2P traffic classification at early stage, which consumes limited computational and memory resources for early stage traffic identification of P2P traffic. Rizzi et al. in 2013 [13] present a very effective neuro fuzzy system to identify early stage traffic. Nguyen et al. [14] further extend the early stage to “timely” for VoIP traffic classification. They derived statistical features from the subflow, while this means that subflow is a small number of packets. In [11], the authors used 20 packets and extract feature at early stage. In ”‡•   ! "   !  ' %$        ! ! !     „  ! ”`•       %$ ? %$  “  +‚   %$    +"#‚ for their study work; they also use the average and standard deviation values %$ “  %$    !   !       !`”¢• %!“   %$  ! "   ! . They say 5–7 packets    ' %$  ! "     #  also say that selecting too many packets will increase the computational %! Œ !  !  %$  !     will decrease accuracies performance results and cannot possess enough information. Bernaille et al. in 2006 [17] studied the problem of effective packets    !   "     !  "   >   used K-means, GMM, and HMM model using the size and direction of the !  %$   #;    !        #   %$  !      '  %$  ! "   ! . They conducted many Œ%          $  '      !  %$  Œ   three machine learning algorithms. Lim et al. in 2010 [18] used not only packet size as features but also connection level and statistical level feature for their study using a number of different datasets while conducting Naïve Bayes, C4.5 decision tree K-nearest neighbors, and Support Vector Machine   Œ%  ! $#   %$    š„%%!‰ !#;‰ >  ! 

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empirical study. During the last few years, Internet user increases day by day due to presence of reliable and free of cost instant messaging and free calling applications on Internet. WeChat application is one of the instant messaging applications available online freely. WeChat is an instant messaging and free calling application developed by Tencent Holding in China. This is a multifunctionality application and can be used both in smart phone and in desktop machine. After launching the WeChat application, its online    !!”‡•   “     November 2015, its active costumer users reached 650 million all over the world while from outside of China its active users reached 100 million [20].  '    %%!  performance of network. It is also important to classify WeChat messages, >' !!  accurately to manage quality of services (QoS). Huang et al. [21] proposed measurement ChatDissect tool to measure … ; %%!  `‘ ¢^}  WeChat from real-world network traces. In 2013, Church and De Oliveira [22] studied the performance of mobile instant messaging sending service with traditional short messages. In 2014 O’Hara et al. [23] studied instant messaging application WhatsApp in smart phone and took some interviews and survey to study the user activity using WhatsApp application. In 2014, X  ! ”˜• !   … ƒ%% %%! ‰     collected data in European Network which consisted of millions of data  ‰ ! ' ‰  "˜§ and Guo [25] studied video messaging services in WeChat and WhatsApp %%! %   !  '   However, no study found out the number of packets that are most effective … ; %%! !  ! "% ' $”¢•> !!… ;  Œ   ' ‰      '  %%! !! % … ;  …   $!'  %  ;ƒ Œ Notice that we capture text messages, picture messages, audio !!>  '  !!   duration of one hour, respectively. Thereafter, we select early 20 nonzero payload packets of every application and save them as features extraction. X    %    '  =§ !  #   !  Œ%! process is given below starting from generate features dataset step up to Wilcoxon test results.

Figure 2.X $ !   ' =§! 

(ii)

Features Extraction. As discussed in related work section that       !   !  %$  “         !%$    ! "    ! ” •"  $>   %$ “     ! … ;  …   %$ “  of 20 packets of early stage of WeChat application. It is noticed that we only used those packets which have nonzero payload packet [16]. For the features, we put the order of feature in a number vice manner such as packet 1, then packet 2, and then up to 20 packets and similarly packet size 1, packet size 2, and up to 20 packet sizes.

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(iii)

Mutual Information. After development of 20 features datasets, we execute mutual information analysis on between the packet “             packets which we label,  % ' !>     %$          mutual information of each feature’s datasets. Through mutual information analysis execution, we are able to know how much      %$   !  know the effectiveness of each feature’s packet. (iv) Generate Feature Datasets. In this section, we make features    %$    %$ “    then second packet with its packets size and so third and up to 20 features datasets. +"    ' %% %  '   . For this research study, we  !   ! !      !    $ !   !!  Œ   '              "  %% >  !       ' %$  number       %%!     !  !  accuracy. Thus we are only interested in the results of packet numbers. (vi) Friedman Test. In this research work, we also use statistical tests to deeply know the effective packets numbers. Friedman tests      Œ               the results of applied number of packets. The detailed study of statistical tests will be demonstrated in Section 5. (vii) Wilcoxon Test. It is also statistical test. We will use this test as we !! X  "      !!    X !   !! …!Œ   the effective number of packets using different number of packets.

METHODOLOGY In this section, we will explain all the applied methods in this paper study.

Mutual Information In the information theory, mutual information is very widely used for features selections [29], image processing [30], speech recognition [31], and

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so on. Mutual information is the measure of mutual dependence between two random variables X and Y which defines the amount of information held by random variable. The mutual information between two variables in information theory can be defined as

(1) In the above equation (1), the marginal entropies of ܺ and ܻ are (ܺ) and (ܻ), respectively. Where conditional entropies are ‫ )ܻ | ܺ(ܪ‬and ‫)ܺ | ܻ(ܪ‬ and joint entropy of ܺ and ܻ is ‫ܺ(ܪ‬, ܻ), respectively, while the connection among ‫)ܺ(ܪ‬, ‫)ܻ(ܪ‬, ‫)ܻ | ܺ(ܪ‬, ‫)ܺ | ܻ(ܪ‬, ‫ܺ(ܪ‬, ܻ), and ‫)ܻ ;ܺ(ܫ‬is shown in X  ?  % >  ' (2) (3) (4) where(‫ )ڄ‬is the probability distribution function of a random variable. As in [32] we use the three equations in (1) and can obtain the computational formula of mutual information. We also used the same method for mutual information as in [32]. (5)

Figure 3. The relationship between mutual information and the entropies.

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However, if the variables are continuous random variable then summation will be replaced by a definite double integral as given.

(6) For mutual information computation analysis, there is abundant free available software on Internet, but we choose for our study Peng’s mutual information MATLAB toolbox [33].

$  >   We conducted our identification experiments using ten well-known and widely used machine learning classifiers. All the selected machine learning classifiers are executed using Weka data mining software [34]. Weka tool is a data mining application used in many areas in computer science and also used by many researchers for network traffic classification [35]. Firstly, we formatted the datasets as a comma separated value “CSV” which is a supported extinction of Weka application. Then using two folders’ cross validation method, we apply all the selected machine learning classifiers. The introductory information about the applied machine learning classifiers is given below and the classifiers selected for this study are shown in Table 3.(i)Bayes: Bayes machine learning classifiers are actually based on Bayes Theorem; Bayes machine learning classifiers are very widely used in computer and engineering area and got very effective results. In this study, we utilized Bayesian network (Bayes Net) [36] machine learning classifier and also Naïve Bayes machine learning classifiers [37, 38].(ii)Meta: in this research work, we used Meta category classifier in Weka tool; we select Bagging [39] and AdaBoost [40] machine learning classifier to classify WeChat traffic accurately. Meta classifier was first trained to learn and then produce strong learning.(iii)Rule: this category algorithm just creates rules using specific policy and then executes classification result testing data. In this category, we select OneR [41] and PART [42] rule base classifier for our study.(iv)Trees: this is also called decision tree algorithms used by many researchers in their research study. It is also called statistical classifiers. In our study, we select J48 also called C4.5 classifiers [43], Naïve Bayesian trees [44], and Random Forest [45].(v)SMO: in function category, we select SOM [46, 47]. SOM is also called a supervised machine learning technique and known as Support Vector Machine, which is widely used in many areas for classification. SVM is useful for both classification and regression.

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Table 3.;!  !   

For more detailed literature review of the applied machine learning ! >  !    ! !     ! literature review.

Statistical Test In more depth to know the effectiveness of packets and to compare the results of the applied ML classifiers as well as to find out the significant difference among the results of the applied method, statistical tests are conducted. In this study, we executed two different statistical tests, Friedman and Wilcoxon test, on the results of methods [48, 49]. The detailed introductions to both Friedman and Wilcoxon statistical tests are given below. (i)

Friedman Test. Friedman test is a statistical test. It is also called Friedman nonparametric test. Friedman test is a kind of %                     !%%!   #   %   is to calculate the test statistics; it converts all the original results of methods to ranks. The process of ranking of this test is that it ranks the best performing on the rank of 1 and then second best 2 and so on. After ranking the average ranking (AR), it is then calculated. If is the rank of then th of k algorithms ini th of n datasets, thus the Friedman test also needs average ranking of algorithms:

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(7) While in null hypothesis all the algorithms behave similarly, ranks should be equal and the distribution x2 is calculated as follows:

(8)

(ii)

where݉ is the number of executions and n is the number of methods. If the distribution of methods is large enough       '!            %%!      !   !!    ­  #    ! ' !  each method and the probability value (‫ ݌‬value) shows the   ! ' !    !!      !  test results. On the other hand, for multiple hypothesis testing we also apply post hoc method to determine hypothesis comparison   !!  ­  %    ! ' !> !  cases lowest hypothesis result is also concerned about rejection. §      ! ! !!  ­  ‫ ݌‬value (APV) and post hoc can be used to search the lowest ‫ ݌‬value for each hypothesis. In this study, we used for post hoc method Holm’s   ”˜ •    '   '     %     !X     $> !  X   using 1×݊ comparison because ݊×݊ comparison is too long to show in this paper. Wilcoxon Test. We also used Wilcoxon signed rank statistical test in this research study. Wilcoxon test is also a nonparametric test used for pairwise comparison between two methods [50]. If di is the difference between two methods performance scores oni th out of n problem and if the score is known in different ranges, then it can be normalized on intervals 0 and 1 in [51]. Thereafter, the difference is ranks by their absolute values while in case of ties practitioners will apply one method in [52]. In this case, the positive values considered that the method performed well and the second one vice versa.

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(9) +

Ÿ


 %' '! 
is the sum of negative difference '! "          
Ÿ
+ is high then the hypothesis will be rejected. This statistical test is also used like Friedman        %  !!  ­   %     '! 


Evaluation Criteria for Performance Measurement For the performance measurements confusion matrix is the base of traffic classification measurements. Figure 4 shows the confusion matrix for traffic classification performance evaluation, wherein rows refer to the actual class of the instances and column refers to the predicted class of instances.

Figure 4.;Œ!  ! '!

#             "     !   confusion metrics are described below step by step:(i)True Positive (TP):      ;!ƒ  !      !  ;!ƒ+‚True

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Negative (TN)  ;!ƒ!   ! Class A.(iii)False Positive (FP)  ;!ƒ!   as belonging to Class A.(iv)False Negative (FN): it means that Class A is not !   !;!ƒ Using the above given metrics, different metrics can be made for the '!  !  %   ”`> `˜•>       '  !  !!“  XXŠ'! ¡ ' >     ƒš;      !!  (i)

Accuracy ;!            ! ! %! ' !!! %! ! '  ! = !!>     of TP and TN divided by the sum of TN, TN, FP, and FN. (10)

ƒ! %     !"    ' !!  '  !  ! (ii)

Sensitivity. Remember that sensitivity and recall are the same   !   \ +˜‚   sensitivity. (11)

" /  3"    %  !   ! ! ! '  !\+`‚   ! !  !!   #Š'  by sum of FP and TN. (12) (iv)

Area under Curve. It is also called receiver operating characteristics +{;‚ '  ”``•>        %       ! ! "!     ! X!$ %  #!!  sensitivity. The AUC values can be computed by using confusion matrix values by TPR and FPR.

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(13)  %  ©ŸX '©#  %!ŸX%  # '>  !!  (14) \+˜‚   ƒš; '   '%  

EXPERIMENTAL RESULTS AND ANALYSIS In this section, we will explain the detailed experimental results and analysis. Firstly, we will explain the mutual information analysis results of HIT Trace I dataset including four subdatasets and then HIT Trace II dataset results, then give the result analysis of applied methods to validate the effectiveness of packets, and lastly give the results of statistical test for effective ML classifier.

Mutual Information Analysis Results Mutual Information Results of the HIT Trace I Dataset Figure 5 shows the mutual information method analysis results. In Figure 3 the mutual information of the first tow packets of text messages and picture messages is higher compared to the mutual information analysis results of audio call and video call packets. The audio call and video call traffic results are no more than 0.1 values. It means that the first two packets are not contributing information, while in text and picture message packet contributes highly compared with 2 to 4 packets. However, in text messages traffic packet numbers 8-9 give high information identification values and in picture messages packets numbers 7-8 give high information identification values while in audio call traffic packets numbers 6-7 give high information identification values and in video call traffic type is very different as compared to other traffic data; in this traffic packet numbers 19-20 give very high identification information. More details of mutual information results are shown in Table 16.

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Figure 5. Mutual information results of HIT Trace I dataset.

Mutual Information Results of the HIT Trace II Dataset Figure 6 shows the mutual information method analysis results with details. In Figure 6 the mutual information of the first two packets of FTP, DNS, and WWW application is higher as compared to the other WTCP, WUDP, and Telnet application. Similarly packets 2-3 are also not contributing very effectively. Its means that packets 1–3 do not give much identification information. However, packets 6 and 17 give very effective identification information and remaining packets are not contributing very well as compared to the other packets. Moreover, with the perspective application FTP and DNS give very effective identification information compared with other applications.

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Figure 6. Mutual information results of HIT Trace II dataset.

Analysis Results of HIT Trace I Dataset  ! " Figure 7 shows the accuracy results of the WeChat Text message dataset while the details results are shown in Table 19. All the applied machine learning classifiers get very low accuracy using first two and three packets of early traffic, because it is very difficult to identify Internet traffic with only few packets. Due to this reason, all the applied machine learning classifiers get very low accuracy results using early few packets. Naïve Bayes, Hoeffbing, and Random Forest get very low accuracy result using early two packets. However, using text messages dataset, we could not conclude that the first packets are more effective for early stage Internet traffic classification. It is worse to say that the first three packets for early stage Internet traffic classification are effective. However, after three packets using first four packets of WeChat text messages dataset all the applied machine learning classifiers get very effective accuracy results except Random Forest and Part ML classifiers. These three ML classifiers get low accuracy results

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using first four packets. Support Vector Machine (SVM) gets low accuracy result compared to other machine learning classifiers. However, all the classifiers give continuously incensements in accuracy results using all the packets numbers. Except Random Forest classifier, this classifier shows poor accuracy results which are not stable. Thus we can infer that the first four and five packets carry enough identification information for early stage classification WeChat text messages dataset. Note that we use two folders’ cross validation method in this study work.

Figure 7. Accuracy results of WeChat text message dataset.

Figure 8 shows the AUC result of WeChat text messages dataset and the  !   #! X   >!        %$   ƒš;    !     ! !  ƒš; !!      !      !  !       %$     %$ #           !  !  give very effective AUC results but some of them such as SMO and OneR =§ !  '    '   !    … ;   Œ    dataset. As discussed in accuracy analysis, the Naïve Bayes, Hoeffding, and Random Forest give low accuracy results, while in AUC result, Naïve Bayes, Hoeffding, and Random Forest give effective AUC results compared to accuracy of WeChat Text dataset. It means that there exists imbalance data in WeChat Text message dataset.

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Figure 8. AUC result of WeChat text message dataset.

Table 4 shows Friedman’s statistical test results for accuracy result. In Friedman’ test result the packet number nineteen is the best performed one in the accuracy results with the lowest ranking values being 1.7555. While comparing the ‫ ݌‬values and adjusted ‫ ݌‬values, the packet numbers 10–12 of adjusted ‫ ݌‬values are less than ‫ ݌‬values and numbers 15–17 are also the same as adjusted ‫ ݌‬value which is less than ‫ ݌‬values. These are the best performance packets. Table 4. Friedman’s test results for WeChat text messages dataset.

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For better understanding the results of Friedman’s test, we also execute Wilcoxon sign rank test. Table 5 shows Wilcoxon pairwise test results for the WeChat text messages dataset. From the table the p value of 20 packets is greater than 0.05 for the accuracy results. Thus we conclude that there is      Œ    !‡%$   packets for the WeChat text messages dataset. Table 5. Wilcoxon pairwise test results for WeChat text messages dataset.

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  "! " Figure 9 shows the accuracy results of the WeChat Pictures Messages dataset and the details results are shown in Table 18. The results of the text messages dataset are different from the pictures messages dataset. The packet number three gives very low significant increase of accuracy results compared with the first two packets.

Figure 9. Accuracy results of WeChat pictures message dataset.

From the results, it is concluded that the packet number three does not give identification results for the accuracy. It is also observed in the result that all the applied classifiers got continuously increment results using all the number of packets except Support Vector Machine (SMO) classifiers, continuously giving random results using all numbers of packets, while OneR classifiers give very poor identification result in the first 12 packets and then their results are continuously increasing. It means that there exist imbalance data in the dataset. The AUC results for the WeChat pictures messages dataset are a little bit similar to accuracy results. In Figure 10 and Table 22 the AUC results are shown, in which all the machine learning classifiers get the same AUC results but only SMO and OneR ML classifiers results are different from the other ML classifiers which hit high AUC results values.

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Figure 10. AUC results of WeChat pictures message dataset.

Table 6 shows Friedman’s test results for the accuracy result of WeChat pictures messages datasets. In the table packet number 18 gives the best performance result for the accuracy. The average ranking result of 18 is 04.3333 values for the accuracy, which are the lowest average ranking results. However observing the ‫ ݌‬value and adjusted PV for the accuracy, the packets number six to seventeen ‫ ݌‬values are less than when compared to adjusted ‫ ݌‬values. Thus these are the best behaving packets number for   !> !    %$    '   %$      ‫݌‬ values are greater than adjusted ‫  !' ݌‬#         difference among the results with accuracy. Table 6. Friedman’s test results for WeChat pictures messages dataset.

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Table 7 shows the Wilcoxon test results for the accuracy results. In the table, it is clear that the packet numbers 13–15 and 20 packets p values are greater than the standard level of 0.05 for the accuracy results. Thus the packet numbers 13–15 and 20 are not significantly different for the WeChat pictures messages dataset. Table 7. Wilcoxon pairwise test results for WeChat pictures messages dataset.

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 #  " Figure 11 shows the accuracy results of all the selected machine learning classifiers for the WeChat Audio Call dataset. Comparing the results of previous datasets, the results of WeChat audio call dataset are very complex as shown in Figure 11. It is also clear from the figure that the first three packets do not gain identification performance effectively, while packet number four gets effective identification performances for accuracy results. Again the SMO machine learning classifiers give random performance results, which are not stable results, while OneR machine learning classifiers give stable performance results after 11 packets and Bayes Net classifiers give effective result using packet number nine while its performance is continuous after 12 packets. The detailed accuracy results are shown in Table 17.

Figure 11. Accuracy results of WeChat audio call dataset.

The AUC results for the WeChat Audio Call dataset accuracy are shown in Figure 12 and Table 21. The AUC results are very simple as compared to   !#    %$ ! ƒš; ! !  

%$   ' ƒš; ! !¡ ' > ={} Š   ! ! ' ! ! ' !ƒš; !   %$    ! ! '   ƒš; !> ! X   ! ! ' '  accurate AUC results for the accuracy.

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Figure 12. AUC results of WeChat audio call dataset.

Table 8 shows Friedman’s test results for the WeChat audio call dataset accuracy. The packet number sixteen gets the lowest average ranking values for accuracy and all the ‫ ݌‬values of packets are less than from the adjusted ‫ ݌‬values except packet numbers 5 and 11. Thus we can say that there is       !> !  …!Œ  ! are shown in Table 9, in which only packet numbers 2–5 and 11–15 get the ‫      >`  ! !'݌‬%$   ! different from the other packets results. Table 8. Friedman’s test results for WeChat audio call dataset.

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Table 9. Wilcoxon pairwise test results for WeChat audio call dataset.

 $  " Figure 13 shows the accuracy results of WeChat video call datasets. The accuracy results of video call dataset are different from the previous datasets accuracy results. The result of this dataset is a little bit complex; however, all the machine learning classifiers get effective accuracy results using all the packets datasets for accuracy. But the result C4.5 decision classifier is completely straight line conducting all the packets datasets. The results of the first two and three packets are lowest using SMO and Naïve Bayes classifiers but after four packets its accuracy result increases continuously.

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Similarly, using Bayes Net, Random Forest, Bagging, and OneR classifiers with packet number 17, all the classifiers give lowest results, but the remaining classifiers get high accuracy results using packet number 17. In Figure 14 and Table 24 we have shown the AUC results for the WeChat video call dataset. The AUC result pattern is simple as compared to accuracy result. Most of the applied machine learning classifiers get the effective AUC result for the video call dataset except OneR and SMO machine learning classifiers, because the results of the OneR and SMO are the lowest compared to other machine learning classifiers, while Table 10 shows Friedman’s statistical test results for the accuracy of WeChat video call datasets. The packet number two gets the highest average rank values compared to other packets average rank results and its value is 05.20. Similarly, all the p values are less than adjusted p values except packet numbers 4-5. It means that there does not exist significant difference among these results with respect to accuracy. Table 10. Friedman’s test results for WeChat video call dataset.

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Figure 13. Accuracy results of WeChat video call dataset.

Figure 14. AUC results of WeChat video call dataset.

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In Table 11, we have shown the Wilcoxon test results for the WeChat video call dataset accuracy. In Table 11, all the p values are less than the standard level 0.05 except packet number 4. It means that the results of the entire packet except 4 packets are significantly different from all other results. Table 11. Wilcoxon pairwise test results for WeChat video call dataset.

Analysis Results of HIT Trace II Dataset Figure 15 shows the accuracy results of HIT Trace II dataset. All the applied machine learning classifiers get low accuracy result for early stage Internet traffic, because it is very difficult to classify Internet traffic using first few packets. However, we are not interested in classification accuracy. We are interested to find out the most effective packet numbers and effective ML classifier. Moreover, packet numbers 13-14 give the same identification results, but its identification information is low as compared to other packets’ accuracy results. However, the accuracy results of packet numbers

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12 and 18 are continuously increasing. It means that their accuracy results are very good as compared to other packets’ accuracy results. Moreover, all classifiers show stable accuracy results, but Random Forest algorithm gives effective results compared to other machine learning classifiers. Thus the first six packets carry enough identification information as well as 15–19.

Figure 15. Accuracy results of HIT Trace II dataset.

The AUC results for the HIT Trace II dataset is shown in Figure 16 and Table 25. The AUC result for the HIT Trace II is very simple as compared to other traces AUC result. For example, only packet number 5 and packet number 17 get low AUC values and all the remaining packets gain good AUC  !  %   %$ ƒš;  ! = ' > } !  ' ! ƒš;%$  ˜!!  ! '  high AUC result for packet number 14. Similarly, for packet number 5 all !  '  ƒš; Œ % ={ ƒ}    !  !  ¡ ' > !!      !  !  '    ƒš; values results for early stage packets. However the detailed AUC results are shown in Table 26.

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Figure 16. AUC results of HIT Trace II dataset.

Table 12 shows Friedman’s test results for the HIT Trace II dataset accuracy. The packet number 18 gets the lowest average ranking values for accuracy and all the p values of packets are less than from the adjusted p values Œ %%$   ‡#       difference among the results, while the Wilcoxon test results are shown in Table 13 in which only packet numbers 16 and 18 get the p values less  `>      %$   !    other packets results. Table 12. Friedman’s test results for HIT Trace II dataset.

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Table 13. Wilcoxon pairwise test results for HIT Trace II dataset.

Analysis Results of Algorithms Table 14 shows Friedman’s test results for the applied machine learning classifiers. The Random Forest machine learning classifier gets the lowest average ranking values as compared to other machine learning classifiers and all the p values are less than from the adjusted p values except Hoeffding, Bayes Net, SMO, and OneR ML classifiers, while the Wilcoxon test results are shown in Table 15 in which only classifiers OneR, Part, C4.5, and Random Forest get the p value less than 0.05, which mean that these classifiers are significantly different from the other packets results.

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Table 14. Friedman’s test results for algorithm.

Table 15. Wilcoxon pairwise test results for algorithms.

Table 16. Mutual information analysis.

Effective Packet Number for 5G IM WeChat Application at Early ...

Table 17.ƒ !… ;   

Table 18.ƒ !… ; %   

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Table 19ƒ !… ;  Œ  

ANALYSIS AND DISCUSSION Although the results of the five applied IM and WeChat traffic datasets are different, with respective accuracy results and AUC results, some information can be learned from the applied five datasets at early stage WeChat traffic classification.(i)From this study, it is clear that analyzing the results of information analysis and classification experiments results analysis of the first three packets for early stage IM do not carry enough identification information.(ii)From the experimental results, the early traffic packets carry enough identification information for the WeChat early traffic classification. However, all the applied machine learning classifiers get very high effective identification performance results using the early stage traffic except Support Vector Machine and OneR machine learning classifiers results are very poor compared to other applied ML classifiers.(iii)Through accuracy results, the classification performance can be easily evaluated for the early stage Internet traffic classification. But in some cases, some classifiers get high identification performance results and in some cases not very effective, it is due to imbalanced datasets.(iv)OneR and SVM classifiers performance is always poor with increase of nonzero payload packet numbers. However, the performance of OneR and SVM classifiers is very different as compared to other machine learning classifiers.(v)However, it is clear from the experimental results that Random Forest gives very accurate results for all the applied datasets.

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CONCLUSION In this paper, we have tried to find out the most effective packet numbers for the IM WeChat early stage traffic classification as well as effective machine learning classifier. Using mutual information analysis five datasets (text messages, picture messages, audio call, and video call traffic), HIT Trace II, and ten well-known machine learning classifiers are applied. According to experimental results, we conclude that the nonzero payload size packets carry enough identification information for WeChat instant messages applications traffic classification. However, the packet numbers 13–19 are effective packets for 5G WeChat application traffic identification. Moreover, the experimental results of the five datasets are different due to different functionality of 5G WeChat application. However, in the results all the utilized datasets are not the same and the first three packets do not carry enough identification information and give very poor results, while for WeChat early stage traffic classification, according to our experimental analysis, the packet numbers 13–19 are most effective packet numbers. While for effective ML classifiers, we conclude that Random Forest machine learning classifier is effective ML classifier for IM early stage traffic classification. #    !! %           ! "     ! ƒ    !  ' !%  !   ' %$  numbers for 5G WeChat application !       > !   !    %$   "     !  increases computational complexity while minimum features will decrease !   ! !    ! should be developed that show how many packets should be used for  "=%%! ! 

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CHAPTER 14

Software Defined Network (SDN) and OpenFlow Protocol in 5G Network

Khaled Alghamdi1, Robin Braun2 1 School of Electrical and Data Engineering, University of Technology, Sydney, Australia. 2 College of Computer Science and Information Technology, Al Baha University, Al Baha, Saudi Arabia.

ABSTRACT The world is moving at a high speed in the implementation and innovations of new systems and gadgets. 3G and 4G networks support currently wireless network communications. However, the networks are deemed to be slow and fail to receive signals or data transmission to various regions as a result of solving the problem. This paper will analyze the use of Software Defined Citation ƒ! > ‘  }>  +‚>    „   Š  $ +„Š‚ and OpenFlow Protocol in 5G Network.Communications and Network,12, 28-40. doi:10.4236/cn.2020.121002. Copyright      ! "#  $!censed under the Creative Commons Attribution International License (CC BY). http:// creativecommons.org/licenses/by/4.0

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Network (SDN) in a 5G (fifth generation) network that can be faster and reliable. Further, in Mobile IP, there exist triangulation problems between the sending and receiving nodes along with latency issues during handoff for the mobile nodes causing huge burden in the network. With Cloud Computing and ecosystem for Virtualization developed for the Core and Radio Networks SDN OpenFlow seems to be a seamless !  !‰   !  There have been a lot of researches going on for deploying SDN OpenFlow with the 5G Cellular Network. The current paper performs benchmarks as a feasibility need for implementing SDN OpenFlow for 5G Cellular Network. The Handoff mechanism impacts the scalability required for a cellular network and simulation results can be further used to be deployed the 5G Network. Keywords: SDN, LTE, Mobile IP, LTE Advanced, OpenFlow, 5G

INTRODUCTION Mobile IP (or MIP) as defined in [1] is a protocol based on the standards specified in RFC 5944 for mobile devices where its IP addresses are maintained as permanent as mobile subscribers move within different networks. IETF RFC 5944 and IETF RFC 4721 describe the Mobile IPv4 implementations. Further, for the next generation mobility implementation "  !"'¢  %   X;¢` In a cellular environment like LTE where there are billions of Mobile Š  ! =! " suffers from several issues and gaps which include lack of support for paging, large signaling load at global level and high update latency during handoff and triangular routing. This can be also seen with other Mobile IP technologies such as Mobile IPv4 and Mobile IPv6. An SDN Controller is a part of the control plane of the SDN Architecture. This can be further understood with elaboration from [2] [3] with Figure 1 as follows.

  „  Š  $+„Š‚{% X! !`^

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Here, the A-CPI and D-CPI are the reference points for interfaces of the SDN Controller with the SDN Application and Data plane respectively. An SDN controller can also develop its capability to communicate with the non-SDN plane and other peer SDN Controllers. A subordinate SDN Controller can also be further developed. The agent here virtualizes and shares the resources underlying it. The agent also at different level of abstraction exposes control over the network. There can be multiple agents for a Network Element in a SDN controller. This leads to developing a use case for the SDN provider as with the reclusive virtualized network. The requirement for recursion implies a hierarchical virtual service of the SDN controllers and includes intermediate interfaces called the I-CPI. In this paper, we will review a simulation scenario for Hand Off Mechanism with SDN controller giving its advantage of deployment in the 5G Network validating the SDN NFV Ecosystem for the Radio Network Management and Core Network Architecture. However, there are limitations with the Mobile IP which has its own challenges for implementing in the network which includes Security issues like Denial-of-Service attack, Theft of information Passive Eaves Dropping, insider attack, etc.

SOFTWARE DEFINED NETWORK (SDN) A software-defined network is a communication device under the networking section that controls the functionality of the networking devices and makes a separation of data. The networking devices     !!       „   network include packet switches, routers, and local area network (LAN)    #   '   '  !!˜^  !  the world, in addition to normal Wi-Fi speeds internationally (see Figure 3). Of course, downloading speed rate really does not even identify the entire facts for mobile phone network performance, however, mainly because it is actually the headline declare that networking sites promote, this is actually the one we determined to utilize as ideally consultant of network overall performance.

Figure 3. Maximum and average latency in 4G and 3G networks [6].

For the mobile phone consumers, upload speed, download speed, and !!   Π ! >!     !   decided by the particular use a customer applies the system to.

THE HANDOVER A handover in a networking infrastructure is the transfer or transmission of data or ongoing call on session between two connected channels working

  „  Š  $+„Š‚{% X! !`^

289

under a cellular network. The major reasons why a handover is conducted include: 

The handover can be performed when one of the users is out of range.  A handover can also be conducted in cases when a base station has accommodated the required users and is full. A handover is essential as it enables a set latency to occur on server interruption time under a cellular network management on mobile devices.

The Requirements of a Handover Framework ‚

2) 3)

X! Œ!  !   l is OAM state number represented in   

'  “ ! ”57,58,59]. The main advantage of OAM over other beamforming techniques is that OAM can have an unlimited number of orthogonal modes, which allows the EW to multiplex multiple data streams over the same spatial channel, thereby, enhancing the

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spectral efficiency and transmission capacity. OAM support a high number of user in mode division multiple access (MDMA) scheme without utilizing extra resources (i.e., frequency, time, and power). The flexibility of OAM to be used in narrowband and wideband frequency hopping scheme makes it an attractive scheme for low probability of interception (LPI) applications. OAM-based MIMO systems have advantages over the conventional MIMO systems in terms of capacity and long-distance line-of-sight (LoS) coverage [60]. Therefore, OAM has great potential for applications in 6G wireless networks.

Coexistence of variable radio access technologies 6G can lead to a ubiquitous networking infrastructure where users would not only be left with the option of selecting the best communication network. Each node in this network would, however, be intelligent enough to sense the conditions of the channel and the specifications of QoS at any other node. For example, the use case and the availability of network will decide the network as cellular, wireless LAN, Bluetooth, and ultra-wideband (UWB), etc. 6G communication standard should, therefore, be designed in such a way that it will converge all of the wireless technologies. Communication with Wi-Fi, Bluetooth, UWB, VLC, UAVs, biosensors, and satellite communications can all integrate into 6G and should fall under one standard such that all of them can connect with each other. The Wi-Fi operating at 2.4 GHz has already entered deeply into IoTs as most of the appliances are now connected through this network [61,62,63]. By merging all these technologies, 6G would be able to utilize the massive infrastructure deployed by previous technologies, which otherwise can cost 6G a huge revenue. The features in the previous technologies,      $   >   % !   >     %> low latency, high reliability, and massive connectivity should be converged in 6G. 6G technology should also keep the trend of offering new services by applying the new technologies, such as AI/ML, VLC, quantum communications (QC), and quantum machine learning (QML). These services may include but are not limited to smart cars, smart homes, smart wearable, and 3D mapping [64]. Figure 4 gives an overview of the evolution of the wireless generation, with timelines, from 1G to 6G with respect to applications, KPIs, network characteristics, and technology. Figure 4a shows that a major leap in the application is observed with 4G. 4G introduced mobile Internet, mobile TV,

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and HD videos. AR/VR, ultra-HD (UHD) videos, wearable devices, vehicleto-infrastructure (V2X), smart city, telemedicine, and IoTs concepts are introduced in 5G. 6G is projected to have applications such as space tourism, Tactile Internet, fully automated cars, holographic verticals, deep-sea sight, digital sensing, and Internet-of-bio-Nano-things (IoBNT). Figure 4b shows that how KPIs are changing with the evolution of wireless generations from 1G to 6G. Figure 4c shows the evolution of the network characteristics with wireless generations. All Internet protocol (IP) and the ultra-broadband  % ˜^#  %! , softwarization, slicing, virtualization, and wireless worldwide web (WWW) are introduced  `^ "    !!     ! >  “> slicing, and virtualization will be introduced in 6G communication systems. Figure 4d depicts the evolution of technologies with the development of wireless communication generations. The initial stage of the wireless communication system is the development of the advanced mobile phone system (AMPS). Global systems for mobile (GSM) and general packet radio systems (GPRS) family is developed in 2G wireless systems. Code-division multiple access (CDMA) family shifted the wireless systems from 2G to the 3G. OFDM with the integration of turbo codes and MIMO systems is the key technology for 4G communication systems. 5G communication systems brought some new technologies such as cloud/fog/edge computing, massive MIMO, SDN, mmWave and sub mmWave (NR) along with lowdensity parity-check (LDPC) and polar codes. ML, AI, blockchain, THz communication, orbital angular momentum multiplexing (OAM Mux), %! =! +=‚   !  ! >  it affects all the network components. However, with the need to create “  '  !'  '   ' %   \  > we can create slices such as slice for automotive, healthcare, utility. Figure 5 gives a pictorial overview of the 6G wireless network that covers all aerial-ground-sea communications. As shown in Figure 5, 6G will make it possible to communicate with the devices with very low datarates such as biosensor and IoTs, and at the same time, it will enable high data rate communication such as HD video transmission in smart cities. Communication will be possible in a fast-moving bullet train, airplane. It also shows that all of the networks will be merged all together. Further, the buildings and surfaces in smart cities can be equipped with the IRS that could enhance the coverage and quality of service (QoS) of each communicating device. For the maritime communication scenarios, the robust underwater data links will enable the communication between ships, submarines, and sensors at the deep sea level [70, 71]. Besides, innovative technologies such as AR/VR, haptics, and ML will further reduce the effect of physical distances around the globe.

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Figure 5. A depiction of space-air-ground-sea based integrated next-generation communication system with a wide range of applications.

Figure 6 depicts a comparative analysis of the network architecture of 5G and 6G. The 6G core network is shown to have upgraded to the basic 5G core network based on intelligence, high computational power, and high capacity. By integrating BSs/APs, satellites, and UAVs, the access network is upgraded similarly. There is a vertical hand-off in 6G in addition to the horizontal as in that of 5G. Besides, fog computing and MEC are an integral component of the 6G network infrastructure, that reduces latency and bandwidth utilization for regularly needed services by a massive number of devices on the user plan.

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Figure 6. A comparative analysis between 5G and 6G network architecture.

POTENTIAL TECHNOLOGIES Based on the vision of 6G and its network architecture, we now elaborate on the key enabling technologies for 6G wireless networks in this section. Various state-of-the-art technologies must be utilized together to enable the key features of 6G.

Quantum communication and quantum ML Quantum technology uses the properties of quantum mechanics, such as the interaction of molecules, atoms, and even photons and electrons, to create devices and systems such as ultra-accurate clocks, medical imaging, and quantum computers. However, the full potential remains to be explored. A quantum Internet is a way of connecting quantum computers, simulators, and sensors via quantum networks and distributing information and resources securely worldwide [72]. In October 2018, the European Commission launched the Quantum Flagship, a 1 billion Euro project for over ten years involving 5000 scientists to support quantum research in the EU with the goal of creating a quantum Internet [72]. For the next decade, the EU plans to develop and deploy a

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secure pan-European QC infrastructure (QCI) to become the backbone of a quantum Internet. QCI will make quantum cryptography a part of conventional communication networks, protecting the EU sensitive data and digital infrastructure and making it possible to exchange information between different countries securely. QCI will combine terrestrial and  !!    >       !   !!     Œ   communication networks linking the strategic sites within and between countries, the satellite segment will be deployed to cover very long distances across a large area. * $   !!       '      infrastructure [73]. It provides the sender and the recipient of an encrypted message with an intrinsically secure random key in such a way that an attacker cannot eavesdrop or control the system. It will secure important   ! '  *;*=§ '  ! ! ¢^  ! channel estimation, channel coding (quantum turbo codes), localization, load balancing, routing, and multiuser transmissions [75]. In the communication network core side, QC and QML can solve complex problems such as multiobject exhaustive search by providing fast and optimum path selection to the data-packets in ad hoc sensor networks and Cloud IoT [76].

Blockchain Blockchain is bringing the revolution to some of the huge industries such as finance, supply chain management, banking, and international remittance [77]. The concept of blockchain is opening new avenues to conduct businesses. Blockchain provides trust, transparency, security, autonomy among all the participating individuals in the network [78]. As far as the telecommunication industry is concerned, innovation in a competitive environment with reduced cost is the most important parameter for the

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successful businesses in the telecommunication industry. The blockchain industry can benefit the telecommunication industry in the following various aspects.

Internal network operations Smart contracts in Ethereum, which is the second generation of blockchain technologies, have revolutionized the automation system in various applications. Smart contracts allow the computer code to automatically execute when a certain event is triggered. Because of this fact, blockchain has an immense attraction for its applications in the telecommunication industry to automate various operations such as billing, supply chain management, and roaming. Blockchain can prevent the fraudulent traffic in the telecommunication network thereby saving a lot of bandwidth and resources and ultimately increasing the revenue of the operators [79]. Blockchain can save time for the telecommunication industries and reducing the cumbersome post-billing audit process applying the smart contracts for the authentication and clearance of the bills. Through this process, telecommunication industries can automate accounting and auditing processes.

Blockchain-based digital services Telecommunication operators can generate new revenues by proving customers with new blockchain-based services such as mobile games, digital asset transactions, music, payments, and other services. Telecommunication industries can also generate some new revenue streams by allowing the customers to transfer money from the user to user [79].

     +. " 

Digital identity verification already costs the government millions of dollars every year. A blockchain-based digital identity verification system can be implemented in the next generation of communication networks which will replace the existing identity verification systems [79].

/"+  "  " 

Next-generation wireless systems aim to provide a variety of new digital services. Blockchain is an attractive application for the complex transactions initiated for these services. Blockchain can also be used in the advertising

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industry by using user information effectively [31]. This will trigger a massive machine-to-machine (M2M) transactions. Telecommunication operators can take the initiative of using blockchain in this specific area and usher the next generation of digital services. The demand for massive connectivity in 6G has triggered the network resource management such as power distribution, spectrum sharing, computational resources distribution as the main challenges [61, 77]. Blockchain can provide solutions to the 6G network in these domains by managing the relationship between operators and users with the application of smart contracts. Similarly, blockchain can solve the unlicensed spectrum management and energy management problems. Blockchain can also be used in seamless environmental protection and monitoring, smart healthcare, cyber-crime rate reduction [80, 81].

Tactile internet With the evolution of mobile Internet, sharing of data, videos are enabled on mobile devices. The next stage is the evolution of IoTs, in which communication between smart devices is enabled. Tactile Internet is the next evolution of the Internet of networks, which integrated the real-time interaction of M2M and human-to-machine (H2M) communication by adding a new dimension of haptic sensations and tactile to this field. Tactile Internet is the term used for transmission of touch over a long distance. Some of the researchers termed it “Internet of Senses” [82]. ITU has termed the Tactile Internet as the Internet of networks with very high performance, ultra-low latency, high reliability, and high security. Tactile Internet will allow the human and the machines to communicate in the real-time with the environment in a certain range. Haptic interactions will be enabled through Tactile Internet. Creating pressure against the skin without any physical object is one of the main challenges for Tactile Internet. One of the methods to produce such a sense of touch is by intense pressured sound waves. Ultrahaptics, a British company, is working on producing the haptic sensation by using ultrasounds [83]. The ultrasonic transducers can create a sense of touch by controlled production of ultrasonic waves by multiple transducers. These transducers integrated with in-depth cameras can detect the position of the body to react accordingly. Microsoft is also working on the development of haptic sensation using air vortex rings, which are resembling speaker diaphragm [84]. The concentrated waves from the tiny holes can move with

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a resolution of 4 inches and to the distance of 8.2 feet [85], which has a greater range and much less precise than the ultrasound system.

Free duplexing and spectrum sharing (FDSS) In previous wireless generations, wireless systems were using either fixed duplexing (TDD/FDD) such as in the case of 1G, 2G, 3G, and 4G or flexible duplexing in the case of 5G [86,87,88]. Whereas, with the progress in the development of duplexing technologies, 6G is expected to use full free duplex in which all users are allowed to use complete resources simultaneously. Users can use all resources (i.e., space, time, and frequency) in a free duplex mode that eventually improves latency and throughput. Presently, government bodies are monitoring the spectrum and allocating the spectrum to the operators. The owner of the spectrum has the full right to use that spectrum. Any other operator cannot use the spectrum allocated   % # !    ";   exponentially with the increase in the number of users, which increases the complexity of the NOMA system. User cooperation in NOMA can be used to alleviate outage problems of weak users and to provide diversity at the expense of more time slots. However, the number of SICs even becomes larger with the number of cooperating time slots. Space-time block codingbased NOMA (STBC-NOMA) is proposed as an alternative to reduce the number of time slots while keeping the same diversity order [89]. Apart from imperfect SIC, the imperfection in the channel state information (CSI) also affects the performance of NOMA systems. We present a comparative analysis of the impact of imperfect CSI on the

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performance of non-cooperative NOMA, conventional cooperative NOMA (CCN), STBC-aided cooperative NOMA, and conventional orthogonal multiple access (OMA) schemes in Figure 7. For a fair comparison between all schemes, we use the same total power budget for all of them. The   !}       ‰ >     % >! 

KEY PERFORMANCE INDICATORS (KPIS) In this section, we discuss the main KPIs of 6G wireless systems. These KPIs include peak data rate, mobility requirements, connected devices per Km22, area traffic capacity, latency, reliability, network spectral, and energy efficiency.

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Peak data rate One of the use cases for next-generation wireless communication is eMBB, which simply implies high data rates. Hence we can download HD videos in a few seconds. The data rate requirements of users are increasing since the birth of wireless communications. As shown in Figure 4, 1G had the data rates of a few kbps, which further increased to a few Gbps in 5G. These data rates are still not enough for some applications. Therefore, we require the development of some standard and communication protocols that have data rates in the range of 10-100Gbps [107].

Mobility More mobility robustness is also required in next-generation communication systems. High data rates should be maintained in highly mobile devices. For instance, if we are moving in the airplanes or high-speed bullet trains, the communication should not be disturbed, and data rates should be maintained. The mobility requirements for 6G, as defined by ITU, is >1000Km/hr [107].

Massive connectivity (devices/Km 22) Another use case for next-generation wireless communication is mMTC. This is the domain where the IoTs comes in and is machine type communication without the interaction of human beings. The calls, messages, and commands are from machine to the other machine. The actions are not carried out by a human. Rather, it is the machines that are communicating with each other. Next-generation wireless networks technology is expected to accommodate 107107 devices/Km22 [108]. Sensor networks and IoTs will be connected to each other in a cooperative way and with several BS. Devices and applications in this category include wearable devices, control and monitoring devices, self-driving cars, smart grids, industrial automation and control devices, and medical and healthrelated devices. The communication between these devices may be through peer-to-peer or cooperative multi-hop relay manner. Different applications or devices require different network infrastructure design which could manage different content-driven applications/network. Therefore, keeping all of these requirements in view, next-generation wireless networks would require a completely different approach for planning and optimization.

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   [$*?@\\] With the increase in the number of connected devices per unit area, demand for the higher capacity channels and back-hauling also increased. A highly dense deployed sensor network produces more than tara bytes (TB) of data on daily basis [107]. This data production needs a high capacity backhauling channel to accommodate the traffic. In the previous wireless generations (1G-to-5G), wireless protocols are    %  %%!¡ ' >    ' !%  ' "#=#;>    '   % <    <    '    # ' "#!  the development of vehicular communication such as autonomous driving termed as V2X (vehicle-to-infrastructure). The vehicle needs to interact with another vehicle, with pedestrians, and many other sensors installed in the vehicle. All these communication needs to be extremely reliable and with low latency and security. Industrial automation is another example where a lot of sensors are communicating and generating a huge amount of data. The   %!¢^=%’”•

Extremely-high reliability and low latency with security (eRLLCS) Low latency means quick and fast communication. We want our packets to be transmitted in a very short amount of time and there should not be much processing delays. The maximum allowable latency in 6G is ßß ”> •#    $ !! !  will require high reliability and ultra-low latency. Future cities will comprise of smart homes, smart cars, smart industries, smart schools/universities, and smart industries. Smart cities will need to be connected to airplanes, ships, bullet trains, and UAVs. Some of the critical applications which include health care, defense sector, monitoring, and surveillance will require ultrareliability and low delay. Online gaming services demand high reliability and low latency. The eRLLCS in 6G wireless systems will integrate the security features with mMTC and URLLC in 5G with greater requirements of reliability of higher than 99.9999999%% (Nine 9’s) [107]. Autonomous vehicles will be connected to each other and the communication between them should be ultra-reliable, otherwise it may lead to the loss of lives in accidents. In 6G systems, a lot of households and other sensors will be communicating with each other also require ultra-reliability to prevent any mishap to occur.

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^ _      The future intelligent wireless network will comprise of intelligent/smart factories, intelligent/smart hospitals, schools, universities, and autonomous robots. This will require a highly spectral efficient network having high computing power. A high-density and high-rate network will require high bandwidth. The scarcity of the bandwidth will increase with the increase of data in the network. X  !!  >  !;+‚    ! !$ !  drone application under harsh offshore environment. In: 2017 XXXIInd ^  !ƒ !  % " !š of Radio Science (URSI GASS). pp. 1–2. IEEE, New York Joyce K, Duce S, Leahy S, Leon J, Maier S (2019) Principles and practice of acquiring drone-based image data in marine environments. Marine Freshwater Res 70(7):952–963

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72. Nawaz SJ, Sharma SK, Wyne S, Patwary MN, Asaduzzaman M (2019) Quantum machine learning for 6G communication networks: state-ofthe-art and vision for the future. IEEE Access 7:46317–46350 73. Rosenthal I (1978) Photochemical stability of rhodamine 6G in solution. Optics Commun 24(2):164–166 74. Al-Qurishi M, Al-Rakhami M, Alamri A, Alrubaian M, Rahman SMM, Hossain MS (2017) Sybil defense techniques in online social networks: a survey. IEEE Access 5:1200–1219 75. Manzalini A (2019) Complex deep learning with quantum optics. Quantum Rep 1(1):107–118 76. El-Latif AAA, Abd-El-Atty B, Hossain MS, Elmougy S, Ghoneim A (2018) Secure quantum steganography protocol for fog cloud internet of things. IEEE Access 6:10332–10340 77. Hewa T, Gür G, Kalla A, Ylianttila M, Bracken A, Liyanage M (2020) The role of blockchain in 6G: challenges, opportunities and research directions. In: 2020 2nd 6G wireless summit (6G SUMMIT). Levi, Finland, pp 1–5. https://doi.org/10.1109/6GSUMM IT49458.2020.9083784 78. Xua H, Klainea PV, Oniretia O, Caob B, Imrana M, Zhang L. Blockchainenabled resource management and sharing for 6G communications 79. Le Y, Ling X, Wang J, Ding Z (2019) Prototype design and test of blockchain radio access network. In: 2019 IEEE International Conference on Communications Workshops (ICC Workshops). pp. 1–6. IEEE, New York 80. Nguyen T, Tran N, Loven L, Partala J, Kechadi MT, Pirttikangas S Privacy-aware blockchain innovation for 6G: challenges and opportunities 81. Hossain MS (2017) Cloud-supported cyber-physical localization framework for patients monitoring. IEEE Syst J 11(1):118–127 82. Berardinelli G, Mogensen PE, Adeogun RO (2020) 6G subnetworks for life-critical communication. 6G Wireless Summit 2020 83. Simsek M, Aijaz A, Dohler M, Sachs J, Fettweis G (2016) 5G-enabled tactile internet. IEEE J Selected Areas Commun 34(3):460–473 84. Miao Y, Jiang Y, Peng L, Hossain MS, Muhammad G (2018) Telesurgery robot based on 5G tactile internet. Mobile Network Appl 23(6):1645–1654

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85. Maier M, Chowdhury M, Rimal BP, Van DP (2016) The tactile internet: vision, recent progress, and open challenges. IEEE Commun Magazine 54(5):138–145 86. Chen S, Beach M, McGeehan J (1998) Division-free duplex for wireless applications. Electronics Lett 34(2):147–148 87. … ‘> Š!  ƒ> § ;> $   +‚ Œ%  ! demonstration of a full-duplex indoor optical wireless communication system. IEEE Photonics Technol Lett 24(3):188–190 88. >§   '%! Œ    interleaved format and timing adjustment control (Sep 1 1998), uS Patent 5,802,046 89. Z!=Š>¡ƒ>Z$„Š‘> ZZ;+ ‚   non-orthogonal multiple access: cooperative use of distributed spacetime block coding. IEEE Vehicular Technol Magazine 13(4):70–77 90. Jamal MN, Hassan SA, Jayakody DNK (2017) A new approach to cooperative NOMA using distributed space time block coding. In: 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC). pp. 1–5. IEEE 91. Akhtar MW, Hassan SA, Saleem S, Jung H (2020) STBC-aided cooperative NOMA with timing offsets, imperfect successive interference cancellation, and imperfect channel state information. IEEE Transactions on Vehicular Technology pp. 1-1 92. Li W, Moore MJ, Vasilieva N, Sui J, Wong SK, Berne MA, Somasundaran M, Sullivan JL, Luzuriaga K, Greenough TC et al (2003) Angiotensin-converting enzyme 2 is a functional receptor for the SARS coronavirus. Nature 426(6965):450–454 93. Hossain MS, Rahman MA, Muhammad G (2017) Cyber–physical cloud-oriented multi-sensory smart home framework for elderly people:      % % ' Z!! !„;%â 94. Guo S, Dong S (2009) Biomolecule-nanoparticle hybrids for electrochemical biosensors. TrAC Trends Anal Chem 28(1):96–109 95. Hossain MS, Muhammad G, Guizani N (2020) Explainable AI and mass surveillance system-based healthcare framework to combat covid-i9 like pandemics. IEEE Network 34(4):126–132 96. Long F, Zhu A, Shi H (2013) Recent advances in optical biosensors for environmental monitoring and early warning. Sensors 13(10):13928– 13948

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97. Abdulsalam Y, Hossain MS (2020) Covid-19 networking demand: An auction-based mechanism for automated selection of edge computing services. IEEE Transactions on Network Science and Engineering pp. 1-1 98. Johnston SF (2008) A cultural history of the hologram. Leonardo 41(3):223–229 99. Li R (2018) Network 2030: Market drivers and prospects. In: Proc. 1st International Telecommunication Union Workshop on Network 2030 100. Scott J, Stevenson A, Lupa H (2012) Space tourism: an acceleration physiologist’s perspective. Aviation, Space, and Environmental Medicine 83(3) 101. Henbest N (2013) Private space travel: Diary of an astronaut in waiting. New Scientist 220(2944):41–43 102. Moro-Aguilar R (2014) The new commercial suborbital vehicles: an %%  '   =' Technol 26(4):219–227 103. Hassan SS, Hong CS (2019) Network utility maximization for 6G maritime communication in deep waters. Journal of Korean Information Science Society pp. 957–959 104. Lu H, Li Y, Chen M, Kim H, Serikawa S (2018) Brain intelligence: go   ! !!  =! Š  $ƒ%%!+‚¢ â` 105. Rüßmann M, Lorenz M, Gerbert P, Waldner M, Justus J, Engel P, Harnisch M (2015) Industry 4.0: The future of productivity and growth in manufacturing industries. Boston Consulting Group 9(1), 54–89 106. Wollschlaeger M, Sauter T, Jasperneite J (2017) The future of industrial communication: Automation networks in the era of the internet of things and industry 4.0. IEEE industrial electronics magazine 11(1), 17–27 107. Sliwa B, Falkenberg R, Wietfeld C (2020) Towards cooperative data rate prediction for future mobile and vehicular 6G networks. In: 2020 2nd 6G Wireless Summit ( 6G SUMMIT). pp. 1–5. IEEE 108. Series M (2015) IMT vision–framework and overall objectives of the future development of IMT for 2020 and beyond. Recommendation ITU pp. 2083–0 109. Qian Y, Chen M, Chen J, Hossain MS, Alamri A (2018) Secure enforcement in cognitive internet of vehicles. IEEE Internet Things J 5(2):1242–1250

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110. Hoglund A, Lin X, Liberg O, Behravan A, Yavuz EA, Van Der Zee M, Sui Y, Tirronen T, Ratilainen A, Eriksson D (2017) Overview of 3GPP release 14 enhanced NB-IoT. IEEE Network 31(6):16–22 111. Hossain MS, Muhammad G (2020) A deep-tree-model-based radio resource distribution for 5G networks. IEEE Wireless Commun 27(1):62–67

CHAPTER 16

A Semidynamic Bidirectional Clustering Algorithm for Downlink Cell-Free Massive Distributed Antenna System

Panpan Qian1 , Huan Zhao1 , Yanmin Zhu1 , and Qiang Sun1,2 1 School of Information Science and Technology, Nantong University, Nantong 226019, China 2 Nantong Research Institute for Advanced Communication Technologies, Nantong 226019, China

ABSTRACT Cell-free massive distributed antenna system (CF-MDAS) can further reduce the access distance between mobile stations (MSs) and remote access points (RAPs), which brings a lower propagation loss and higher multiplexing gain. However, the interference caused by the overlapping coverage areas of distributed RAPs will severely degrade the system performance in terms Citation: Panpan Qian, Huan Zhao, Yanmin Zhu, Qiang Sun, “A Semidynamic Bidirectional Clustering Algorithm for Downlink Cell-Free Massive Distributed Antenna System”, Wireless Communications and Mobile Computing, vol. 2021, Article ID 6618126, 11 pages, 2021. https://doi.org/10.1155/2021/6618126. Copyright: © 2021 by Authors. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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of the sum-rate. Since that clustering RAPs can mitigate the interference, in this paper, we investigate a novel clustering algorithm for a downlink CF-MDAS with the limited-capacity backhaul. To reduce the backhaul burden and mitigate interference effectively, a semidynamic bidirectional clustering algorithm based on the long-term channel state information (CSI) is proposed, which has a low computational complexity. Simulation results show that the proposed algorithm can efficiently achieve a higher sum-rate than that of the static clustering one, which is close to the curve obtained by dynamic clustering algorithm using the short-term CSI. Furthermore, the proposed algorithm always reveals a significant performance gain regardless of the size of the networks.

INTRODUCTION Background and Related Work Recently, with the widespread adoption of smartphones and the popularity of multimedia services, mobile data traffic is exploding. As current cellular networks are reaching their breaking point, there is an urgent need to develop new innovative solutions [1]. In cell-free massive distributed antenna systems (CF-MDASs), the antennas are distributed over the intended coverage area. Meanwhile, it has a very large number of remote access points (RAPs) which can use a direct measurement of channel characteristics to serve all mobile stations (MSs) in the same frequency band [2]. It is expected to be a key technology enabler of the sixth generation (6G) mobile communication systems [3]. In CF-MDAS, a large number of MSs in the whole area will be served simultaneously by a large number of separately distributed RAPs, which coordinate with the central processing unit (CPU) [4]. In contrast to the traditional DAS [5], CF-MDAS can further reduce the access distance between MSs and RAPs, which brings low path loss and high spatial multiplexing gain. However, CF-MDAS brings more serious inter-RAP interference, especially in the overlapping area than that of the conventional DAS. Due to the collaboration among RAPs, the system performance based on the sum-rate can be optimized effectively in this way. Nevertheless, it requires the complete channel state information (CSI) of all RAPs processed jointly, a strict synchronization across RAPs, and strong information exchange backhaul capability. Thanks to RAP clustering, which is rated as one of the promising techniques to combat inter-RAP interference for CF-MDAS [6, 7], the scale of collaboration can be reduced and backhaul

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burden by sharing full CSI between a limited number of RAPs can be diminished as well. ^  !!> ! !  ! !  and dynamic clustering. Static clustering is formed according to the geographic locations of the BSs without any CSI [8, 9]. The number of   !  } !  Œ >     '   Though static clustering is simple enough and does not rely on fast backhaul, =   ! !!   ! ‰   Several studies on dynamic clustering have investigated to overcome the above mentioned problems, Shi et al. proposed a dynamic user-centric cell clustering algorithm [10], which can not only cancel the joint intracluster interference but also effectively alleviate the overall and per-BS cooperation cost. However, it can only count on short-term CSI. In [11], the authors proposed a clustering algorithm based on maximum coordination gain which focuses on minimizing the intercell interference to the cell-edge MS. Nevertheless, the clustering algorithm ignores the bidirectional cooperation gain between RAPs. The authors in [12] proposed a bidirectional dynamic   $+}„Š‚%'      '  % !    (SE) performance. It is worth noting that even dynamic clustering can be exploited to achieve higher cooperative gains than static clustering, but its complexity is very high. It is also noted that most of the existing clustering algorithms are unidirectional. One cluster chooses the best cluster freely that can bring high channel gain to itself. At the same time, the dynamic forming cooperative clusters will result in frequent changes of clusters and lead to a large signaling overhead, which is based on the short-term CSI. Furthermore, in the CF-MDAS, owing to the limited-capacity backhaul, sharing the short ;"!!ƒ !

Motivation and Contributions In the existing literature, we investigate the clustering problem of the CFMDAS with limited-capacity backhaul, aimed at maximizing the system sum-rate. Since traditional dynamic clustering algorithms cannot be applied to the CF-MDAS directly, it can only consider unidirectional clustering and depend on the short-term CSI. To this end, we propose a semidynamic bidirectional clustering algorithm using long-term CSI. The main idea of the algorithm is to cluster RAPs according to the bidirectional average rate gain among clusters. Our novelties and contributions can be summarized as

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follows:(i)We develop a network model where each MS is randomly placed into the network. Compared to most bibliographies where the number of MSs in each RAP is fixed, we consider the number of MSs is different in each cluster(ii)We derive a closed-form expression for the average rate per MS based on some approximation techniques, which can be computed with a low computational complexity(iii)Based on the derived expression, we proposed that the process of the semidynamic bidirectional clustering algorithm can be approximately equivalent to combining two clusters with the maximum bidirectional average rate gain per MS in each iteration(iv)We propose a semidynamic bidirectional clustering algorithm for the downlink cell-free CF-MDAS. The proposed algorithm can reduce the backhaul burden and obtain a higher sum-rate with long-term CSI. Simulation results show that our proposed algorithm can achieve a higher sum-rate than the static clustering. Furthermore, our proposed algorithm achieves a performance very close to the optimum curve obtained by dynamic clustering algorithm with the short-term CSI The remainder of this paper is organized as follows. In Section 2, the system model used in this study is described. A semidynamic bidirectional clustering algorithm is proposed in Section 3. Then, we discuss the simulation assumptions and compare the performance of different RAP clustering algorithms in Section 4. Finally, the paper is concluded in Section 5. For notations, matrices and column vectors are denoted by bold capital letters X and bold letters x, respectively. The transpose and Hermitian transpose are denoted by and , respectively. The F × F identity matrix is denoted by IF. The vector 2-norm of x is represented by . The space of all M × N matrices with complex entries is represented by CM×N. A combination of k elements taken from n different elements is presented by . A complex Gaussian distribution function with mean 0 and variance ¼'  . The Gamma distribution function with the shape %  ß ! %  ±'  . The cardinality of a set U is denoted by . The expectation operation is denoted by .

SYSTEM MODEL In this section, the general system model for the CF-MDAS is introduced including the network model, channel model, and signal model descriptions, respectively. Then, the ergodic achievable sum-rate is given.

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Network Model We consider a 2-dimension downlink CF-MDAS which consists of RAPs and K MSs as shown in Figure 1. RAPs are located at the center of each hexagon, where each RAP is equipped with Nt antennas. Define R1 as the distance between one RAP and any vertex of its hexagon. Therefore, the distance between two nearest RAPs is . MSs are distributed randomly in the network, and each MS is equipped with a single antenna. We define the number of RAPs along each dimension is the size of network, and it can be changed. A simple illustration of clustering is given in Figure 1, the RAPs in the same color hexagons form a cluster. If a RAP is associated with no MS, it is assumed to be sleeping. Let be the set of clusters, where L is the number of clusters. All MSs choose the best RAP with the maximal largescale fading. Denote the set of MSs in cluster i as Ui , where # >  =  !!   .

Channel Model The channel vector between RAP m in cluster i and MS k in cluster i is noted as (1)

Figure 1: A 2-dimension CF-MDAS.

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where gmiki denotes the small-scale fading with independent and  !!   +‚ !  >  æmiki denotes the large-scale fading, which can be modeled as (2) where f miki is a log-normal shadow fading variable between RAP m and =$>¦ % ! Œ% >miki is the distance between RAP m and MS k.

Signal Model The received signal vector of MS k in cluster i is

(3) where hki is the composite channel vector from all RAPs in cluster i to is the composite channel MS k in cluster i noted as vector from all RAPs in cluster j to MS k in cluster i, wki is the beamforming '  =$!    jki , and sk is the data symbol with unit variance destined to for MS k. zki is the noise . P is the average transmit power of each following the distribution RAP.

Ergodic Sum-Rate The intracluster interference can be cancelled by using zero forcing (ZF) beamforming, that is, (4) where

is

the kth column of , and is the compound channel matrix between RAPs in cluster i and MSs within the cluster. Therefore, the signal-to-interference-plus-noise ratio (SINR) of MS k is

(5)

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Then, the downlink rate of MS k can be expressed as (6) With above observations, the ergodic achievable sumrate of the system can be presented as

(7)

SEMIDYNAMIC BIDIRECTIONAL CLUSTERING ALGORITHM As stated previously, dynamic clustering based on short-term CSI will lead to a large signaling overhead among RAPs and MSs, making it infeasible in practical systems. Therefore, we propose to form clusters based on longterm CSI. In this section, we first derive the asymptotical average rate per MS associated with long-term CSI, which will be used in the following clustering algorithm design. In what following, we analyze the bidirectional cooperation willingness and the complexity of the optimal clustering by exhaustive search (ES) algorithm. Finally, a semidynamic bidirectional clustering algorithm is proposed.

Average Rate per MS In this subsection, we first employ a Gamma approximation technique pioneered in [13, 14] to obtain the distributions of both the signal and the interference terms. Based on the distributions, we derive the asymptotical average rate per MS in the high-SINR regime. The useful channel strength can be denoted as

(8) where is distributed as a chi-square random variable (RV) with 2Nt degrees of freedom scaled with 1/2 [14], thus . Therefore, is a sum of independent Gamma RVs which does not yield a mathematically tractable expression. Fortunately, the sum of independent nonidentically distributed Gamma

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RVs can be well approximated by employing the second-order matching technique shown in the following lemma. are independent Gamma RVs with Lemma 1 (see [13]). Assume shape and scale parameters ßi and ±i> èi xi can be approximated  ^ ‹«       ·% !       large-scale MIMO systems with ZF receivers,” IEEE Transactions on Vehicular Technology, vol. 66, no. 6, pp. 4834–4844, 2017. 15. W. A. Mahyiddin, N. A. B. Zakaria, K. Dimyati, and A. L. A. Mazuki, “Downlink rate analysis of training-based massive MIMO systems with wireless backhaul networks,” IEEE Access, vol. 6, pp. 45086– 45099, 2018. 16. Y. Dhungana and C. Tellambura, “Performance analysis of SDMA with inter-tier interference nulling in HetNets,” IEEE Transactions on Wireless Communications, vol. 16, no. 4, pp. 2153–2167, 2017. 17. Q. Zhang, S. Jin, M. McKay, D. Morales-Jimenez, and H. Zhu, “Power allocation schemes for multicell massive MIMO systems,” IEEE Transactions on Wireless Communications, vol. 14, no. 11, pp. 5941– 5955, 2015.

CHAPTER 17

Resource Allocation for SWIPT Systems with Nonlinear Energy Harvesting Model

Yifan Hu, Mingang Liu, and Yizhi Feng School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, China

ABSTRACT In this paper, we study the resource allocation for simultaneous wireless information and power transfer (SWIPT) systems with the nonlinear energy harvesting (EH) model. A simple optimal resource allocation scheme based on the time slot switching is proposed to maximize the average achievable rate for the SWIPT systems. The optimal resource allocation is formulated as a nonconvex optimization problem, which is the combination of a series of nonconvex problems due to the binary feature of the time slot-switching Citation:Yifan Hu, Mingang Liu, Yizhi Feng, “Resource Allocation for SWIPT Systems with Nonlinear Energy Harvesting Model”, Wireless Communications and Mobile Computing, vol. 2021, Article ID 5576356, 9 pages, 2021. https://doi. org/10.1155/2021/5576356. Copyright: © 2021 by Authors. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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ratio. The optimal problem is then solved by using the time-sharing strong duality theorem and Lagrange dual method. It is found that with the proposed optimal resource allocation scheme, the receiver should perform EH in the region of medium signal-to-noise ratio (SNR), whereas switching to information decoding (ID) is performed when the SNR is larger or smaller. The proposed resource allocation scheme is compared with the traditional time switching (TS) resource allocation scheme for the SWIPT systems with the nonlinear EH model. Numerical results show that the proposed resource allocation scheme significantly improves the system performance in energy efficiency.

INTRODUCTION In traditional energy-constrained wireless networks, the wireless devices normally use batteries as energy source and require periodically recharging or replacing the batteries, which is difficult for a large number of wireless devices and even hazardous or impossible in some circumstances [1], resulting limited lifetime of the wireless devices and the networks. Energy harvesting (EH) that allows the energy-limited wireless devices to harvest energy from the ambient environment is a promising solution for extending the lifetime of energy-constrained wireless networks. Among the EH technologies, simultaneous wireless information and power transfer (SWIPT) takes advantage of the radio frequency (RF) signal’s ability of carrying both information and energy at the same time, providing great convenience of recharging to energy-constrained devices by harvesting energy from the RF signals. SWIPT is especially suitable for the wireless terminals with low-power consumption whereas hard to access. The SWIPT technique has gained wide attention from both researchers and engineers since Varshney proposed the idea in 2008 [1]. In [2], the trade-off between the amount of the harvested energy and the achievable rate is studied for the SWIPT systems in the frequency selection channel with additive white Gaussian noise (AWGN). In [3], two kinds of SWIPT receivers, namely, time switching (TS) and power splitting (PS) receivers, are, respectively, proposed. Since they were proposed, the TS and PS receivers have attracted a lot of interest due to the simplicity of realization [4–8]. %  !!>      %  !!    <    SWIPT in multicarrier systems. In [10], the secrecy rate maximization is studied in an OFDM secrecy communication system. A multiuser OFDM system for maximizing the sum rate with a minimum transmit power constraint is designed in [11]. In [12], an optimal resource allocation policy is derived in a generalized WPCN where the devices can harvest energy from both multiple-antenna power station and ambient energy harvesting. " ”•>        maximization is considered in large-scale multiple-antenna SWIPT systems. In [14], the authors propose an energy   Œ“%“   ! !  energy-constrained amplify-and-forward (AF) multirelay network. In [15],   <   %“  ="={ <  ! networks with SWIPT. Most of the aforementioned works about SWIPT systems consider the ! ¡ !>   % '     ¡ receiver is assumed to be a constant. However, it is found that the power '     %!X  +„;‚'   is affected by the input power, i.e., when the input power is greater than a certain threshold, the output power changes nonlinearly with the input power and shows a saturation characteristic [16]. Hence, the linear EH model cannot properly model the practical EH implementations and may lead to the resource mismatch in the resource allocation or the overestimation in the system performance evaluation [17]. In [16, 18], the parametric and logistic function-based nonlinear EH model and the piece-wise linear EH model are, respectively, proposed to capture the nonlinear saturation input-output characteristic of the practical EH circuit, which are further exploited for the ' ”‡â•%  !!>”‡•>   ­ transmit power allocation and receive power splitting for SWIPT systems with the realistic nonlinear EH model. Considering a Nakagami-m channel,

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the authors investigate the outage probability and reliable throughput of a multiuser wireless-powered SWIPT system in [20]. In [21], the analytical results on outage probability performance are presented for a cooperative relay-aid network with spectrum sensing and energy harvesting. In [22], the power split factor is optimized to minimize the outage probability for the AF relay system with PS receiver and the nonlinear EH model. In [23], considering imperfect channel state information (CSI) conditions, the authors analyse the outage probability for the multirelay SWIPT systems with PS receivers and the nonlinear EH model. In this paper, we consider the single-input single-output (SISO) pointto-point SWIPT communication systems with TS receiver. The piece-wise linear EH model is considered to model the nonlinear saturation input-output characteristic for the EH circuit. We propose a simple optimal resource allocation scheme based on the time slot-switching strategy to maximize the average achievable rate for the systems. The information transmission block time T is divided into N time slots. Each time slot is used for information decoding (ID) or EH according to the optimal scheme. The optimal problem ! ' Œ%“>   %'  the time-sharing condition and then solved by using the time-sharing strong duality theorem and Lagrange dual method. Compared with work [24] where the linear EH model is considered, the major contribution of our work is that we consider the effect of the saturation characteristic of the practical nonlinear EH model and derive a more realistic optimal resource allocation scheme. The rest of this paper is organized as follows. “System Model” introduces the system model. The optimal resource allocation design is proposed and the optimal problem is solved in “Design of Resource Allocation Optimization Algorithm.” In “Numerical Results,” the numerical results and discussion are presented. “Conclusions” concludes this paper.

SYSTEM MODEL We consider a SISO point-to-point SWIPT communication system as shown in Figure 1, where both the receiver Rx and the transmitter Tx have single antenna and Rx is assumed to be energy-limited and could harvest energy from the received signals with a TS scheme. We assume that the channel between the transmitter and receiver is subjected to frequency flat and the block Rayleigh fading. The channel coefficient is denoted as h, which is a random variable following the complex Gaussian distribution with zero

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 ' ¼2. Without loss of generality, we assume that each time !  !“     "     !>   #  æ  equal to 0 or 1, indicating that the receiver implements EH or ID operations, respectively. The received signal can be expressed as (1) where P is the transmit power, x is the data symbol with unity power, i.e., where E[] means the mathematical expectation, is Ÿ    ! >±© represents the path loss where d is the distance between the source and the destination nodes, and m represents the pathloss exponent. The achievable rate of the system based on the TS scheme can be expressed as (2) where

is the channel power gain.

Figure 1. The SISO point-to-point SWIPT system.

We use the piece-wise linear function to model the nonlinear saturation input-output characteristic of the EH receiver. The harvested power at the EH receiver is then given as [17, 18]

(3) where       '          harvester in the linear region, Ps is the maximum saturation harvested power of the EH receiver. As shown in (3), when the conversion power of the energy receiver ¦±¡ exceeds the saturation output power Ps, the output power of the energy receiver remains unchanged and some of the power

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is wasted, which means that in such case, the time slot should be switched to the ID receiver rather than EH receiver to avoid the waste of the power. Hence, the resource allocation scheme should be redesigned for the practical SWIPT system considering the nonlinear input-output characteristic of the EH circuit.

DESIGN OF RESOURCE ALLOCATION OPTIMIZATION ALGORITHM In this section, we propose an optimal resource allocation scheme based on the simple time slot-switching strategy to achieve the balance between the maximum average achievable rate and the maximum average harvested energy. From (3), the harvested energy can be expressed as (4) We consider maximizing the average achievable rate for the SISO SWIPT systems as shown in Figure 1. The optimization problem is formulated as (5)

(6) where is the minimum amount of the harvested energy required to maintain the normal operation of the EH receiver. "  !> !!'  %!%! +`‚ !  the combination of a series of nonconvex problems due to the binary feature æ"> %! Œ!' %“%!  a numerical calculation method increases exponentially with thenumber of the time slots. In this section, to solve the optimization problem (5), we use the time-sharing strong duality theorem proposed in [25], which is given as follows. Theorem 1. Time-Sharing Strong Duality Theorem [25]. If the time%< '  % % % '   + ` / $*  /; $j   the strong duality is always true, i.e., the duality gap is zero.

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…  %'   %“%! +`‚   ! æz©æx æŒ %!!  = Qx>  ! !æ“!  problem (5) when Equations (7) and (8) +‚ …   ì Æ ì > !  ¡k denote the channel power gain of the time slot. The average achievable rate and the average harvested energy can be, respectively, expressed as

(9) where , æj,k is the value of æj $  !„   integer M such that , where means ceiling round operation. Let

(10) When N ืî>   =ืî+Šâ=‚ืî# > Æ‫(א‬0, 1), it can be derived that

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(11)          !  ! æz     \ +‚ Similarly, it can be derived that

(12)      ! !æz \+ ‚X (11) and (12), Proposition 1 is proved.   %“%! +`‚    according to the Time-Sharing Strong Duality Theorem, the primal problem (5) has the same optimal solution as its dual problem and can be solved by the Lagrange dual method. The Lagrange function of (5) can be expressed as (13)   Ç ¿     §  !%!      ”+ æ‚ ¿ * Accordingly, the Lagrange dual function can be expressed as

(14) The dual problem is then given as

(15) "   ' !!'  ' !%! >   %!  the optimization problem (14) into parallel N subproblems that has the same

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structure as (14). The kth (k = 1, 2, ‫ڮ‬N) subproblem can be expressed as (16) where . In order to solve the optimization problem (16), we compare the value of the objective function  æ©æ© 0, which can be expressed as

(17)  % ' !¡  > %!!%! +˜‚>æ‫ כ‬can be expressed as

(18) # >   '  '!  Ç> æ‫ כ‬can be obtained from (18) according to     !        ! §  Ç‫ כ‬be the optimal dual variable, which is associated with the required minimum harvested energy value   \! +`‚# %!!'! Ç‫ כ‬can be obtained by iterative search and updating until the average energy collection meets the minimum energy constraint, i.e., , for which the detailed iterative search algorithm will be discussed later. The proposed resource allocation scheme is based on the optimal TS strategy according to the channel state in each time slot. To describe the %!   + ‚ ! !>+‚+ ‚>    two functions G1 and G2 with respect to the channel power gain

(19)

(20) \ +‡‚     !'             are, respectively, logarithmic and linear functions of H. In this for paper, we solve it by traversing the value of H from 0, when the difference    '!     ! +  Ÿ`),

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we consider the values of the two terms to be equal, i.e., = 0; then, we can obtain an approximate nonzero real root H1. Next, it can be found         > by derivation that and the position of the point that changes its monotonicity is related to the '! Ç{'!>  Ç $  %+>¡1). At this time, since G1(0) = 0, G1(H1) = 0,when , from (3) ‫כ‬ and (18), >  >æ = 1. Similarly, it can be obtained by derivation that is an increasing function, when , where H2 is the nonzero real root of equation . Then, it can be deduced that when , ‫כ‬ æ = 0. Therefore, the optimal TS strategy can be expressed as

(21) In (21), the optimal TS thresholds H1 and H2 depend on the optimal dual '! Ç‫כ‬, which is determined by the inequality constraints in (5) and should be chosen so that . The average energy collection can be expressed as

(22) where is the probability density function (pdf) of H, Hth = Ps/¦±. #  '   !  %!!'! Ç‫ כ‬is summarized !! ƒ! # !'! Ç0 is set as 1:0, and ðÇ is set as 0.01. Algorithm 1." '   ! Ç‫כ‬.

The optimal resource allocation scheme can then be described as follows. Firstly, the information transmission block time T is divided into N time

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!# >   !>> $ +Ì$ÌŠ‚ !> %! TS thresholds H1 and H2 are determined by using Algorithm 1. Finally, the channel power gain Hk is compared with H1 and H2 $ +Ì$ÌŠ‚  slot; if Hk< H1 or Hk¿¡2, the receiver switches to information decoding or else switches to energy harvesting. The optimal resource allocation scheme is summarized as follows in Algorithm 2. Algorithm 2. Resource allocation algorithm for the SWIPT systems with nonlinear EH model.

Remark. It is shown in (21) and Algorithm 2 that for the SWIPT systems with nonlinear EH model, the optimal resource allocation scheme based on the time slot-switching strategy requires that the receiver switches to information decoding when the signal-to-noise ratio (SNR) is larger or smaller, whereas switching to energy harvesting is performed in the region of medium SNR.

NUMERICAL RESULTS In this section, we present simulation results for the proposed optimal resource allocation scheme for the SWIPT systems with nonlinear EH model. In order to validate the proposed scheme, we compare the proposed resource allocation scheme with the traditional TS resource allocation scheme [26]        performance for the SWIPT systems with nonlinear ¡ !>         !#  tained by sweeps, the time switching factor from 0 to 1 with a step 0.01, and          (23) š!      %  >    %       © …> and the required minimum harvested energy is set to = 5 mW. The  '     !   % %  ! ¡  '   ¦© ©˜…”•>  % ' !# '  '   ^ ¼2 = N0,

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 Š  Š©’Š0. The information transmission block time T is divided into N = 105subslots, and in each subslot, the channel obeys the Rayleigh distribution and is independent of each other. The distance between the source node and the destination node is set to d =5m, and the pathloss exponent m is 2.0. X             versus the transmit power P for the SISO SWIPT systems with nonlinear EH model under various SNRs. "     %   >       decreases in the region of middle and higher SNRs (i.e., SNR = 15 or 25 dB), whereas it keeps almost invariant when the SNR is smaller, i.e., SNR = 5 dB. It can be observed that compared with traditional TS resource allocation   >   %%     !!     ! %'      %         X  >     ! observed that the gap of system performance between the proposed scheme and traditional TS scheme becomes more obvious as the SNR increases, indicating that the proposed performs better in the region of higher SNR. The reason is that, when the SNR increases, the EH receiver is more likely to work in the nonlinear region. Due to the saturation characteristic of the nonlinear EH model, the traditional TS scheme is more likely to waste the received power in such case, thus resulting in the larger performance gap.

Figure 2.      '   %     "{ …"# systems with various signal-to-noise ratios (SNRs).

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In Figure 3, the energy efficiency of the SISO SWIPT systems with nonlinear EH model under various SNRs is plotted against the distance d between the source and the destination nodes. It can be observed that the energy efficiency decreases when the distance d increases. It shows similar results in Figure 2 that the proposed resource allocation scheme substantially outperforms the traditional TS resource allocation scheme and that the performance gap gets larger when SNR increases. Moreover, it can be observed that as the distance d increases, the energy efficiency of both schemes and the performance gap between the two schemes tend to be zero in the lower SNR region, since as the distance d increases, the received power of the signal becomes very weak; thus, the achievable rate is very small in the lower SNR region for both the schemes.

Figure 3.    '   "{…"#    various signal-to-noise ratios (SNRs).

X ˜         versus the pathloss exponent m for the SISO SWIPT systems with the nonlinear EH model under various SNRs. ! !  ' X           

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when the pathloss exponent m increases. The reason is that a larger value of m means more transmit loss of the power of the signal, which brings a lower received SNR and smaller achievable rate for the system. It can be also observed that compared with the distance d, the system performance is more susceptible to the variation of the pathloss exponent m, since the pathloss exponent m has a greater impact on pathloss than the source-destination distance d. Also, it is shown that the proposed resource allocation scheme substantially outperforms the traditional TS resource allocation scheme for !! % ! Œ% >            and the performance gap between the two schemes tend to be zero in lower SNR region when the pathloss exponent m increases.

Figure 4.    ' % ! Œ%  "{…"#tems with various signal-to-noise ratios (SNRs).

"X `>       versus the minimum required harvested energy is shown for the SISO SWIPT systems with the nonlinear EH model under various SNRs. Not surprisingly, the proposed scheme substantially outperforms the traditional TS scheme despite the variation of the minimum

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 \  '    ">           the traditional TS scheme decreases with the increases of the minimum required harvested energy , whereas for the proposed time slot-switching   >       $ %!  !    minimum required harvested energy increases from 1 mW to 10 mW.

Figure 5.    '  \  '      SISO SWIPT systems with various signal-to-noise ratios (SNRs).

CONCLUSIONS In this paper, we have studied the resource allocation scheme for the pointto-point SISO SWIPT systems with the nonlinear EH model. We have proposed an optimal resource allocation scheme based on the time slot switching to maximize the average information rate for the systems, with which the receiver performs information decoding in the region of higher or lower SNRs, whereas switching to energy harvesting is performed in the region of medium SNR. Compared to the traditional TS resource allocation

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scheme, the proposed scheme significantly improves the system performance in energy efficiency, and the system performance improvementsgets larger when the SNR is higher. We have investigated the impacts of the sourcedestination distance d and the pathloss exponent m on the energy efficiency performance. Results have demonstrated that the system performance is more susceptible to the variation of the pathloss exponent m than the distance d. We have also investigated the impacts of the minimum required harvested energy on the energy efficiency performance. It is demonstrated that for the traditional TS scheme, the system energy efficiency decreases with the increases of the minimum required harvested energy , whereas for the proposed time slot-switching scheme, the system energy efficiency is hardly affected by the variation of the minimum required harvested energy . In our setup, we consider the SISO SWIPT systems and Rayleigh channels for the proposed optimal scheme. MIMO systems and other more complex channel models for the proposed scheme can be further studied in future work.

ACKNOWLEDGMENTS This research was funded by the National Natural Science Foundation of China (grant numbers 61871192 and 61871191) and the Science and Technology Program of Guangzhou (grant number 201904010373).

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REFERENCES 1.

L. R. Varshney, “Transporting information and energy simultaneously,” in 2008 IEEE International Symposium on Information Theory, pp. 1612–1616, Toronto, ON, Canada, July 2008. 2. P. Grover and A. Sahai, “Shannon meets Tesla: wireless information and power transfer,” in 2010 IEEE International Symposium on Information Theory, pp. 2363–2367, Austin, TX, USA, June 2010. 3. R. Zhang and C. K. Ho, “MIMO broadcasting for simultaneous wireless information and power transfer,” IEEE Transactions on Wireless Communications, vol. 12, no. 5, pp. 1989–2001, 2013. 4. L. Liu, R. Zhang, and K. C. Chua, “Wireless information and power transfer: a dynamic power splitting approach,” IEEE Transactions on Communications, vol. 61, no. 9, pp. 3990–4001, 2013. 5. H. Ju and R. Zhang, “A novel mode switching scheme utilizing random beamforming for opportunistic energy harvesting,” IEEE Transactions on Wireless Communications, vol. 13, no. 4, pp. 2150–2162, 2014. 6. C. Shen, W.-C. Li, and T.-H. Chang, “Wireless information and energy transfer in multi-antenna interference channel,” IEEE Transactions on Signal Processing, vol. 62, no. 23, pp. 6249–6264, 2014. 7. X. Zhou, R. Zhang, and C. K. Ho, “Wireless information and power transfer in multiuser OFDM systems,” IEEE Transactions on Wireless Communications, vol. 13, no. 4, pp. 2282–2294, 2014. 8. Q. Shi, L. Liu, W. Xu, and R. Zhang, “Joint transmit beamforming and receive power splitting for MISO SWIPT systems,” IEEE Transactions on Wireless Communications, vol. 13, no. 6, pp. 3269–3280, 2014. 9. D. W. K. Ng, E. S. Lo, and R. Schober, “Wireless information and power        optimization in OFDMA systems,” IEEE Transactions on Wireless Communications, vol. 12, no. 12, pp. 6352– 6370, 2013. 10. G. Zhang, J. Xu, Q. Wu, M. Cui, X. Li, and F. Lin, “Wireless powered cooperative jamming for secure OFDM system,” IEEE Transactions on Vehicular Technology, vol. 67, no. 2, pp. 1331–1346, 2018. 11. S. Yin and Z. Qu, “Resource allocation in multiuser OFDM systems with wireless information and power transfer,” IEEE Communications Letters, vol. 20, no. 3, pp. 594–597, 2016. 12. Q. Wu, G. Zhang, D. W. K. Ng, W. Chen, and R. Schober, “Generalized wireless-powered communications: when to activate wireless power

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transfer?” IEEE Transactions on Vehicular Technology, vol. 68, no. 8, pp. 8243–8248, 2019. – ; > – …>  – ; > · <    %“  wireless information and power transfer in large-scale MIMO systems employing energy beamforming,” IEEE Wireless Communications Letters, vol. 2, no. 6, pp. 667–670, 2013. A. Gupta, K. Singh, and M. Sellathurai, “Time-switching EH-based joint relay selection and resource allocation algorithms for multiuser multi-carrier AF relay networks,” IEEE Transactions on Green Communications and Networking, vol. 3, no. 2, pp. 505–522, 2019. * §  § «> · %“       in MIMO two-way relay networks with SWIPT,” IEEE Systems Journal, vol. 14, no. 1, pp. 196–207, 2020. E. Boshkovska, D. W. K. Ng, N. Zlatanov, and R. Schober, “Practical non-linear energy harvesting model and resource allocation for SWIPT systems,” IEEE Communications Letters, vol. 19, no. 12, pp. 2082– 2085, 2015. Y. Feng, M. Wen, F. Ji, and V. C. M. Leung, “Performance analysis for BDPSK modulated SWIPT cooperative systems with nonlinear energy harvesting model,” IEEE Access, vol. 6, pp. 42373–42383, 2018. Y. Dong, M. J. Hossain, and J. Cheng, “Performance of wireless powered amplify and forward relaying over Nakagami-m fading channels with nonlinear energy harvester,” IEEE Communications Letters, vol. 20, no. 4, pp. 672–675, 2016. J. M. Kang, I. M. Kim, and D. I. Kim, “Joint Tx power allocation and Rx power splitting for SWIPT system with multiple nonlinear energy harvesting circuits,” IEEE Wireless Communications Letters, vol. 8, no. 1, pp. 53–56, 2019. S. Gao, K. Xiong, R. Jiang, L. Zhou, and H. Tang, “Outage performance of wireless-powered SWIPT networks with non-linear EH model in Nakagami-m fading,” in 2018 14th IEEE International Conference on Signal Processing (ICSP), pp. 668–671, Beijing, China, August 2018. T. L. N. Nguyen and Y. Shin, “Outage probability analysis for SWIPT systems with nonlinear energy harvesting model,” in 2019 International Conference on Information and Communication Technology Convergence (ICTC), pp. 196–199, Jeju Island, Korea (South), October 2019.

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22. K. Wang, Y. Li, Y. Ye, and H. Zhang, “Dynamic power splitting schemes for non-linear EH relaying networks: perfect and imperfect CSI,” in 2017 IEEE 86th Vehicular Technology Conference (VTCFall), pp. 1–5, Toronto, ON, Canada, September 2017. 23. J. Zhang and G. Pan, “Outage analysis of wireless-powered relaying MIMO systems with non-linear energy harvesters and imperfect CSI,” IEEE Access, vol. 4, pp. 7046–7053, 2016. 24. L. Liu, R. Zhang, and K. C. Chua, “Wireless information transfer with opportunistic energy harvesting,” IEEE Transactions on Wireless Communications, vol. 12, no. 1, pp. 288–300, 2013. 25. W. Yu and R. Lui, “Dual methods for nonconvex Spectrum optimization of multicarrier systems,” IEEE Transactions on Communications, vol. 54, no. 7, pp. 1310–1322, 2006. 26. X. Zhou, R. Zhang, and C. K. Ho, “Wireless information and power transfer: architecture design and rate-energy tradeoff,” IEEE Transactions on Communications, vol. 61, no. 11, pp. 4754–4767, 2013.

CHAPTER 18

A Resource Allocation Scheme with Delay Optimization Considering mmWave Wireless Networks

Marcus V. G. Ferreira1, Flávio H. T. Vieira1,2, Marcos N. L. Carvalho2 1

Instituto de Informática, Universidade Federal de Goiás, Goiania, Goiás, Brasil

2

Escola de EngenhariaElétrica, Mecanica e de Computação, Universidade Federal de Goiás, Goiania, Goiás, Brasil

ABSTRACT This paper presents a resource allocation scheme for wireless networks, aiming at optimizing the users’ data delay. It is proposed to provide optimal delay al-location by solving an optimization problem using idle state prediction and considering 5G characteristics such as mmWave propagation. The perfor-mance of the resource allocation algorithm is verified and Citation:Ferreira, M.V.G., Vieira, F.H.T. and Carvalho, M.N.L. (2020), A Resource Allocation Scheme with Delay Optimization Considering mmWave Wireless Networks. International Journal of Communications, Network and System Sciences, 13, 105-119. doi: 10.4236/ijcns.2020.137007. Copyright      ! "#  $!censed under the Creative Commons Attribution International License (CC BY). http:// creativecommons.org/licenses/by/4.0

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compared with oth-ers from the literature using computational simulations in terms of Quality of Service (QoS) parameters such as throughput, delay, fairness index, loss rate and computational complexity. In these simulations, it is also considered the mmWave propagation and carrier aggregation technology for wireless next generation systems, in order to verify the system performance in a high data rate scenario. Keywords:- Delay, QoS, mmWave, Carrier Aggregation, 5G

INTRODUCTION Aiming to support next-generation wireless networks, we address in this paper a scenario with millimeter waves (mmWaves) above 6 GHz and a radio frame structure with spacing between sub-carriers of 120 kHz. The 5G specifications of Release 15 of the 3rd Generation Partnership Project (3GPP) standardization organization were followed, in order to verify the performance of the algorithm in a current simulation scenario with a high transmission rate [1] [2] [3]. In this work, it is also considered in the simulations, techniques such as 256-QAM (Quadrature Amplitude Modulation) and carrier aggregation. These technologies are introduced in Long-Term Evolution - Advanced (LTE-A) networks and developed by operators to promote higher data rates, better coverage, and lower latency [4]. There are many proposals for resource allocation schemes in wireless networks [5] [6] [7]. LIN SU et al. (2012) [7] proposed a coding scheme based on the Particle Swarm Optimization (PSO) heuristic to map particle !    %    “    %  ' !  %!   !    !    a penalty function. GUAN et al. [6] proposed an algorithm that aims to guarantee the minimum transmission rate criteria required by the user. This ! > !!  *!   '  +*‚      %% >  estimates the number of blocks required for each user and then allocates these blocks to users according to their priorities. FERREIRA et al. (2015) [5] presented an algorithm, namely Min-delay algorithm, that considers estimated system delay values, channel quality and the maximum delay value for each user in order to decide on the scaling of available resources. However, these algorithms do not apply optimization methods to directly minimize system delay for users as our proposal does.

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In the mentioned context of resource allocation, the delay is considered essential, especially for real-time applications with variable transfer rates and specific band requirements such as the Voice over Internet Protocol (VoIP) and videoconference services. It is also important to highlight the need to develop new techniques to facilitate the rigorous QoS requirements for 5G networks in terms of delays to be met [8]. The present paper proposes a resource block allocation scheme for wireless networks considering current and next-generation techniques, aiming at optimizing the system delay and obtaining values for other QoS parameters compatible with those of other schedulers from the literature. In this work, it is proposed to provide optimal delay allocation for networks with 5G characteristics by solving an optimization problem using idle state prediction and by considering carrier aggregation and mmWave propagation. Comparisons are made with other algorithms in the literature   ! *%  >%'     of the proposed algorithm.

TRANSMISSION SYSTEM MODEL Consider a wireless network with only one evolved Node B (eNB), consisting user of N items of User Equipment (UE) such as can have different Carrier Aggregation (CA) capacities, which are modeled as , where μn represents the maximum number of Component Carriers (CC) that can be supported by . All UEs in a given Transmission Time Interval (TTI) compete for M where orthogonal CCs without overlapping, represented by each has a different number of Resource Blocks (RBs)Æm . Assuming that the maximum Modulation and Coding Scheme (MCS) index of all UEs in different RBs is modeled as in [4]; that is: (1) where

 %   Œ=; Œ½næ m in the

RB p. The value of depends on the channel quality interval between 0 and h. In turn, the uplink value of h is 22, its downlink value is 28, and P = ŒÆƒ  !$!!Œ!  Œ [4]:

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(2) representing the RB allocation map where

if and only if the RB

%;;æm!! !½n and otherwise. The resource !! Œ ƒ  !           \       equation (3), i.e., two or more UEs cannot use the same RB simultaneously [4]:

(3) ƒ;;!!Œ       !$ !!  to which UEs is represented as a binary matrix M ×N relative to the total number of CCs M and the number of UEs N, given by [4]:

(4) where   %       æm       ½n . To  %   =;!! ½n ##"> Œ   as [4]:

(5) where  %   =; Œ!! ½æ   ##" #  ^ %    ”‡•          %    MCS index. In this work, to represent the relationship between the MCS index and the transfer rate, the notation is used, where R scans each MCS index for each transfer rate according [9]. In other words, r is the transfer rate obtained for each UE in an RB with MCS b. Therefore, the user transfer       

, i.e., an M ×N matrix where

%     % }½næm . The eNB is responsible for the entire admission procedure, resource scheduling, and link adaptation. After receiving the Channel State Information (CSI) of all UEs, a resource allocation map is built, and the

Resource Allocation for SWIPT Systems with Nonlinear Energy ...

387

MCS index is determined by the eNB and then sent to each UE through the control channels. According to the MCS index, the modulation type and the encoding rate for each UE in each attributed CC can be determined. The ^%  ”‡•     %  =; Œ Many allocation schemes are based on the maximization of the total system throughput with the constraint of the minimum transfer rate required %  "  > %“­ '     throughput provided in a given TTI t, and can be calculated as:

(6) where subject to:

in the TTI t, respectively. This is

(7)

(8) (9) (10) for represents the condition if æm ½> %   =; Œ!!  ½næm for each TTI. Equation (7) ensures that each RB in the network is attributed to one UE at most. Equation (8) guarantees that the number of CCs attributed to each UE is smaller than its aggregation capacity. Equation (9) guarantees that the MCS index for each UE in each CC is lower than the maximum MCS index supported by each RB attributed to its corresponding CC. Equation (10) guarantees that the schedulers meet the minimum required transfer rate for each user.

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RESOURCE ALLOCATION WITH DELAY OPTIMIZATION Consider at first the optimization problem for a static channel h and the initial status of buffer q (t = 1) . The mean arrival rate is denoted as , and it is assumed that no packages arrive after t = 0 . Then, the size of the so that the buffers observation window T is selected with are completely unoccupied in the time window. In this work, it is proposed to obtain an optimal delay scheduling scheme by solving the following optimization problem:

(11) subject to: (12)

(13) where denote the size of the queue and the transfer rate of the user n at time t, and C denotes the instantaneous capacity region of the system. The downlink maximum data rate for an UE transmission in Cyclic  Π\   \     

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!     Œ%  '! ó    % region. Thus, the optimization problem (throughput maximization with ‚ !' ƒ!       optimal values for r that attain the capacity region given by Equation (18). In other words, this work proposes a scheduling algorithm that is Algorithm 1, for resource allocation of wireless networks that is based on Equation (15) for estimating optimal rate values that should be allocated to  >   ;! Œ     ‰               and availability of resource blocks. On the other hand, the PSO algorithm presents the worst performance in terms of the loss rate, tending to provide higher loss rate as the number of users is increased. We believe that this behavior of the PSO algorithm is due to the combination of its lower throughput and fairness values in general than the other approaches.

Figure 11. Processing time for TDL-A channel modeling.

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Figure 12. Processing time for Rayleigh channel modeling. Table 2. Percentage loss rate (%) for TDL-A and Rayleigh channel modeling. Channel modeling

Algorithm

TDL-A

Rayleigh

Number of users 10

20

30

40

50

QoS guaranteed

0

0

0.001

0.001

0.001

PSO

0

0

0

0.025

0.166

Min-delay

0

0

0.001

0.001

0.001

Proposed

0

0

0

0.001

0.001

QoS guaranteed

0

0

0.001

0.001

0.001

PSO

0

0

0

0.004

0.073

Min-delay

0

0

0.001

0.001

0.001

Proposed

0

0

0

0.001

0.001

The complexity of the QoS guaranteed and Min-delay algorithms are given in function of the number of block allocation executions subject to the

Resource Allocation for SWIPT Systems with Nonlinear Energy ...

401

number of users K and blocks N, that is, the complexity is . In the analysis of the complexity of the PSO-based scheduler, as being an heuristic algorithm, the number of iterations maxit and the size of the population Po must also be considered. Thus, the computational complexity of the PSObased scheduler is equal to [14]. In the complexity of the proposed algorithm, the number of executions of the optimization step u to  %!ô¦ !  % !  !  Thus, its computational complexity is equal to , that is slightly higher than those of the QoS guaranteed and Min-delay algorithms, but lower than that of the PSO-based scheduler, as can be seen from the processing time shown in Figure 11 and Figure 12.

CONCLUSIONS This paper proposed a resource block allocation scheme for mmWave wireless networks using current and next-generation techniques, aiming at optimizing the user data delay and maintaining high throughput levels. It was demonstrated that the scheduling policy can be formulated as a total throughput maximization problem and that the choice of weights assigned for computing user rates impacts the delay optimization performance. Another contribution of the present study consists of evaluating the resource allocation algorithm for a scenario with subcarrier aggregation and mmWave propagation, comparing its performance with other algorithms in the literature and with Rayleigh channel modeling. In fact, the proposed resource allocation algorithm demands lower processing capacity in relation to an algorithm based on particle swarm but with better overall performance, as observed in the simulations.According to the results, the proposed resource allocation algorithm with delay optimization presented a better performance in terms of delay for all simulated scenarios with different numbers of users when compared to the PSO, QoS guaranteed and Min-delay algorithms. The proposed algorithm also presents higher total and average throughput values than the other algorithms, loss rate close to zero and suitable fairness values. #   !%'   %% !    %%! delay optimization, despite the transmission channel conditions.

ACKNOWLEDGEMENTS The authors would like to thank Fundação de Amparo à Pesquisa no Estado de Goiás (FAPEG) for their support in the development of the research.

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REFERENCES 1.

3GPP (2017) 3GPP TR 38.901 Version 14.0.0 Release 14. 5G; Study on Channel Model for Frequencies from 0.5 to 100 GHz. 2. 3GPP (2018) 3GPP TS 38.211 Version 15.2.0 Release 15. 5G; NR; Physical Channels and Modulation. 3. Zaidi, A. A., Baldemair, R., Tullberg, H., Bjorkegren, H., Sundstrom, L., Medbo, J., Kilinc, C. and Da Silva, I. (2016) Waveform and Numerology to Support 5G Services and Requirements. IEEE Communications Magazine, 54, 90-98.https://doi.org/10.1109/ MCOM.2016.1600336CM 4. Rostami, S., Arshad, K. and Rapajic, P. (2015) A Joint Resource Allocation and Link Adaptation Algorithm with Carrier Aggregation for 5G LTE-Advanced Network. 2015 22nd International Conference on Telecommunications (ICT), Sydney, 27-29 April 2015, 102-106. https://doi.org/10.1109/ICT.2015.7124665 5. Ferreira, M.V.G., Vieira, F.H.T. and Abrahao, D.C. (2015) Minimizing Delay in Resource Block Allocation Algorithm of LTE Downlink. 2015 International Workshop on Telecommunications (IWT), Santa Rita do Sapucai, 14-17 June 2015, 1-7.https://doi.org/10.1109/ IWT.2015.7224550 6. Guan, N., Zhou, Y., Tian, L., Sun, G. and Shi, J. (2011) QoS Guaranteed Resource Block Allocation Algorithm for LTE Systems. 2011 IEEE 7th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), Wuhan, 10-12 October 2011, 307-312.https://doi.org/10.1109/WiMOB.2011.6085383 7. Su, L., Wang, P. and Liu, F. (2012) Particle Swarm Optimization Based Resource Block Allocation Algorithm for Downlink LTE Systems.   ƒZ ­ Island, Korea, 15-17 October 2012, 970-974. 8. Gupta, A. and Jha, R.K. (2015) A Survey of 5G Network: Architecture and Emerging Technologies. IEEE Access, 3, 1206-1232.https://doi. org/10.1109/ACCESS.2015.2461602 9. 3GPP (2016) 3GPP TS 36.213 Version 13.0.0 Release 13. LTE; Evolved Universal Terrestrial Radio Access (E-Utra); Physical Layer Procedures. 10. 3GPP (2018) 3GPP TS 38.104 Version 15.2.0 Release 15. 5G; NR; Base Station (BS) Radio Transmission and Reception.

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11. Zhou C. and Wunder, G. (2007) Delay and Throughput Optimal Scheduling for OFDM Broadcast Channels. Proc. IEEE Globecom 2007. IEEE International Symposium on Information Theory (ISIT2007), Nice, France, 24-29 June 2007. 12. Ericsson (2019) In the Race to 5G, CP-OFDM Triumphs. In-the-Raceto-5G-CP-OFDM-Triumphs, May 2019, 27.https://www.ericsson. com/en/blog/2017/5/ 13. 3GPP (2019) 3GPP TS 38.306 Version 15.7.0 Release 15. 5G; NR; User Equipment (UE) Radio Access Capabilities. 14. Abrahão, D.C. (2018) Escalonamento de recursosemredes LTE utilizandoprocesso envelope de tráfegomultifractal e curva de serviçomínima. PhD Thesis, Escola de EngenhariaEletrica, Mecanica e de Computação—EMC (RG).

Index

Symbols 5G cellular handset devices 45 5G technology 3, 6, 7, 8, 9 A Access Points (APs) 114 additional parameter 220, 229 additive white Gaussian noise (AWGN) 364 advanced mobile phone system (AMPS) 313    ! ! $‡>  ! ! $+ƒX}‚‡ angular width 69 anisotropy 61 application programming interface (API) 285 architectural integration 95  ! !!  > Augmented Reality (AR) 14 automatic machine learning 244

B bandwidth 44, 45, 47, 48, 49, 52, 53, 54, 56 baseband processing 4 base-band processing units (BPUs) 308 baseband unit (BBU) 291 Base Stations (BSs) 116 bias voltage 60, 62, 65, 66, 68, 69, 71, 72 Big Data Analytics 93, 94, 100, 102, 104, 110 blockchain 210, 214, 216 block chain technology 215 Building Baseband Unit (BBU) 14 business ecosystem 135, 136, 137, 138, 139, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155 business model 138 business segment 220

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C capacitors 60 Capital and Operational expenditures (CAPEX and OPEX) 116 Cell-less architecture 304 cellular network 12, 14, 15, 16, 20, 40 cellular operator 17 ; !{  +;{‚˜ channel bandwidth 44, 45 channel coding 162, 165, 166, 167, 168, 170, 172, 191 Channel Quality feedback Information (CQI) 119 Channel Quality Indicator (CQI) 391 channel stacking arrangements 85 channel state information (CSI) 310 Channel State Information (CSI) 115, 117 checksums 221 circuit design 44 Clear Channel Assessment (CCA) 120 client side 211 Cloud Computing 93, 94, 95, 110 ! ¢> cloud platform 139 clustering technique 243 code division multiple access (CDMA) 163 commercialization 209    ‡ Communication Engineering 43, 56 communication modes 12 Communications Society 94 computational complexity 78, 79, 86, 88

computational formula 250 computational power 305, 317 Computer Science 241, 246, 278 connected slot antenna array (CSAA) 71 constant function 229 consumer smart terminals 147 Conventional 4G technology 44 convergence 94, 109 conversion power 367 cooperative relationship 137 cooperative research development 148 coordinated multi-point (CoMP) 307 Core Network Architecture 285 corporate campus 100 !% Œ >‡>  ;! Œ`

!  ˜ Finite impulse response (FIR) 82 Five Generation (5G) networks 60 E ‰ %% ecosystem structure 148 ‰ !  ˜ educational resources 198, 201 ‰ Œ!>` educational transformation 198 frequency bands 45, 56 electromagnetic waves (EW) 311 frequency range 60, 61, 65, 70, 71, electronic learning 198 72 energy consumption 219, 220, 221, frequency spectrum 44, 78, 80 232, 236, 237 fuzzy control 230       ¢˜> ¢`> > fuzzy logic 220, 227 374, 375, 376, 378, 379, 380 fuzzy number 227, 228, 230, 231, energy harvesting (EH) model 363 232, 233, 240 energy minimization 118 G energy receiver 367 Enhanced Mobile Broadband game theory 114 (eMBB) 163 Gaussian distribution 366 environmental information 211 general packet radio systems (GPRS) Ethernet protocols 220 313 evidence-based medical education general purpose processors (GPPs) 198 164 evolved packet core (EPC) 315 Global System for Mobile (GSM) Expansion development 143 162 exponential modulation 82, 85 guarantee bit rate (GBR) 105

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H handset development 45 Heterogeneous Networks (HetNets) 113, 114 Hidden Markov Model 106 hierarchical virtual service 285 High altitude stratospheric platform station (HAPS) 7

  +¡„‚ hybrid Automatic Repeat Request (HARQ) 164 I image processing 249 industrial application 139 industrial chain 212, 213 industry development 137 information analysis 243, 249, 274, 276 information and communication technology (ICT) 139 information decoding 364, 365, 366, 373, 377 information theory 249 information transmission process 215 infrastructure deployment 95 instant messaging (IM) 243 intelligent algorithms 304 intelligent Internet 5 " !!   ‰  es (IRSs) 311 intelligent terminal 139 International Telecommunication Union (ITU) 13, 162 International Telecommunication Union Radiocommunication sector (ITU-R) 303

Internet of Things (IoT) 12 Internet protocol version 6 (IPv6) 6 "   ˜>˜>˜>˜˜> 248, 254, 258, 270, 276, 277, 278, 279 "     !   ˜> 243, 244, 258, 277 Internet user 245 internship 203 interpacket time (IPT) 244 inter symbol interference (ISI) 80 inverse fast Fourier transformation (IFFT) 80 inverse functions 228 its aggregation capacity 387 K Key Performance Indicators (KPIs) 306 L large-scale device connectivity 202 licensed spectrum 114, 116 lightweight protection 5 linear antenna array 59 linear functions 371 liquid crystal (LC) 59 local area network (LAN) 285, 297 !  >>> ! !% ` logistics industry 209, 210, 211, 212, 213, 214, 215, 216 Long Term evolution 77 long-term evolution (LTE) 44 Long Term Evolution (LTE) 114, 162 low-density parity-check (LDPC) 313

Index

Low Density Parity Check (LDPC 162 low probability of interception (LPI) 312 M machine learning 96, 102 Machine-Type-Communications (MTC) 12 Macro base stations 294 management side 211 marginal entropies 250 maritime communication 316, 341 market value 9 massive foreseen deployment 113 massive machine type communication (mMTC) 302 Massive machine type communications (mMTC) 163 massive number 302, 307, 317, 331 mathematical expression 122 medical education 197, 198, 200, 201, 202, 203, 205 medical teaching 147 medical technology 147 medical undergraduate education 200 Microwave Studio software 55 minimum mean square error (MMSE) technique 183 mobile applications 106 mobile broadband 93, 95, 96, 97, 109 mobile communication 60 Mobile communication 3 mobile consumer 4 !  ¢

409

Mobile Edge computing (MEC) 13 mobile information technology 201 Mobile IP 284, 285, 292, 293, 295, 298 mobile market 7 mobile node 292 mobile phone consumers 288 mobile phone networks 288 mobile phones 203, 204 mobile registers 292 mobile revolution 7 Mobile Users (MUs) 116 modern logistics 210 modulation functions 81 monotonicity 372 motor-skill acquisition 204 multi-access edge computing (MEC) 12, 21 multicarrier transmission techniques 77 multilayer stacked patch 62 multinational operators 141 Multipath Transmission Control Protocol 219 MultiPath Transmission Control Protocol 220 multiple inputs and multiple outputs (MIMO) 310 multiple-input single-output (MISO) 364 mutual information 242, 243, 249, 250, 251, 256, 257, 277, 280, 281 Mutual information 250, 257, 258, 274, 281 mutual information analysis 242, 243, 249, 256, 277 mutual relationship 243

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N narrowband IoT (NB-IoT) 303 narrow bandwidth 62 National Tsing Hwa University (NTHU) 99 natural disasters 200 Nearly perfect reconstruction (NPR) 82 network architecture 146 network capacity 12, 20   $  network energy 220 Network function virtualization 315 network function virtualization (NFV) 12 network information society 202 networking devices 285 networking infrastructure 288 networking sites promote 288 network management data 101 network performance 390 network reliability 231 network slicing mechanism 15   $ ˜>˜>˜‡>`> 279   $     New Radio (NR) 161, 162 non-line-of-sight (NLOS) 311 non-Orthogonal Multiple Access (NOMA) 162 Non-Standalone (NSA) 162 Novel implementation 165 O occupation measurements aggregation 120 Online tertiary 199 Open Wireless Architecture (OWA) 4

operating frequency 44, 45, 47, 49, 51 operational expense (OPEX) 96 operations expenditure 125 optical transmission technology 144 orbital angular momentum multiplexing (OAM Mux) 313 orbital angular momentum (OAM) 311 Ordered Fuzzy Numbers 221, 227, 229, 240 orientation number 231 orthogonal frequency division multiplexing 79 orthogonal frequency-division multiplexing (OFDM) 365 Orthogonal frequency division multiplexing (OFDM) 308 Orthogonal Frequency Division Multiplexing (OFDM) 77, 80 orthogonal multiple access (OMA) 163 P packet data convergence protocol (PDCP) 289 packet number 241, 243, 247, 249, 260, 262, 263, 265, 266, 268, 270, 271, 272, 279 packet transmissions 219 Particle Swarm Optimization (PSO) 384 permittivity 61, 62, 72 physical channels 116, 117, 120, 132 physical downlink shared channel (PDSCH) 162 Physical Medium Dependent (PMD) 120

Index

planar antennas 62 points of communication 220 polarization 44 popularization 210, 215 post-processing block 84 probability distribution function 250 professionalism 145 programmability 315 prototype 162, 165, 167, 168, 184, 189 %%  ! %  %%  ! ‡> > >   proximity-instant wireless networking 310 Public Land Mobile Networks (PLMN) 4 Q Quadrature Amplitude Modulation (OQAM) 78 quality of experience (QoE) 12 Quality of Service 5 quantum communications (QC) 312 quantum machine learning (QML) 302, 312 R     `>`>` Radiation pattern analysis 53, 55, 56 radiation patterns 44, 68 Radio Access Network (RAN) 97 radio access points (RAP) 291 radio access technologies (RAT) 308 Radio Access Technology (RAT) 114 Radio Network Management 285 Radio Remote Unit 14

411

radio resources 114 radio transmitter 220 random variable 250, 251 rapid development 203, 205 rate-splitting multiple access (RSMA) 309 Rayleigh channel modeling 394, 395, 396, 397, 398, 400, 401 receiver 220, 226, 236  ‰    ˜` regional synergy 148 relay-aid network 366 remote areas 204 remote regions 291 resource allocation 16, 17, 18, 20 resource management 15 Revenue 123 revolutionary changes 136 robust network 302, 303 Rohacell layer 61, 62, 63, 65, 72 S safe transmission 215 Satellite communication 201 schedule meetings 225  !<   self-healing 304, 308, 328 self-organization 308, 314, 315 serial to parallel (S/P) converter 80 Shapley value 114, 116, 122, 123, 125, 126, 129, 132 short-range communication 16 signal-to-noise ratio (SNR) 364, 373 signal vanishing 60 !   ˜¢ simultaneous wireless information and power transfer (SWIPT) 363, 364

412

5G and 6G Communication Technologies

single antenna parameters 63 single-input single-output (SISO) 364, 366 slot-switching strategy 366, 368, 373 Smart logistics tracing system 213, 215 soft decision algorithms 164    „   Š  $ +„Š‚ 14, 283, 284, 285, 289, 291    „   Š  $ +„Š‚ 93, 94     +„‚¢   ˜¢>˜ > video messaging 245, 280 video transmission 302, 308, 316, 330, 334 Virtualization 94 virtual machines (VM) 14 virtual network functions (VNFs) 316 Virtual Reality (VR) 14 visible light communication (VLC) 306

413

Vodafone 145 Voice over Internet Protocol (VoIP) 385 voltage standing wave ratio, high gain (VSWR) 44 W warehousing 210, 212, 213 WeChat application 242, 245, 246, 248, 277 wideband antenna array 59 wideband frequency 61, 62, 72 wireless communication systems 45 Wireless Fidelity (WiFi) 113, 114 wireless networks 4, 5 World Wide Wireless Web 6 wristwatches 305

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