Wireless Communications Systems Architecture. Transceiver Design and DSP Towards 6G 9783031192968, 9783031192975

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Synthesis Lectures on Engineering, Science, and Technology

Khaled Salah Mohamed

Wireless Communications Systems Architecture Transceiver Design and DSP Towards 6G

Synthesis Lectures on Engineering, Science, and Technology

The focus of this series is general topics, and applications about, and for, engineers and scientists on a wide array of applications, methods and advances. Most titles cover subjects such as professional development, education, and study skills, as well as basic introductory undergraduate material and other topics appropriate for a broader and less technical audience.

Khaled Salah Mohamed

Wireless Communications Systems Architecture Transceiver Design and DSP Towards 6G

Khaled Salah Mohamed Siemens Digital Industries Software Fremont, CA, USA

ISSN 2690-0300 ISSN 2690-0327 (electronic) Synthesis Lectures on Engineering, Science, and Technology ISBN 978-3-031-19296-8 ISBN 978-3-031-19297-5 (eBook) https://doi.org/10.1007/978-3-031-19297-5 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

To the memory of my beloved Father Eng. Salah Mohamed Abouelhassan, who has raised me to be the person I am today, who taught me how to effectively and efficiently think. Thank you for all the unconditional love that you have given me, encouraging me to achieve all my dreams. Thank you for everything.

Preface

There are many books which focus on digital wireless communications. However, there are not a lot of books dedicated to transceiver architecture. Thus, this book describes and focuses on the wireless communication systems from a transceiver and digital signal processing perspective. It is intended to be an advanced and thorough overview for key wireless communication technologies. A wide variety of wireless communication technologies are addressed. Different communication paradigms and architectures are discussed. Moreover, it covers state-of-the-art wireless communication standards. The book takes a practical systems approach breaking up the technical components of a wireless communication system such as compression, encryption, channel coding and modulation. In this book, mathematical principles are combined with practical system design. This book presents the foundation of wireless communications transceiver from basic principles to the latest standards. The hardware components of a wireless communication system will be explained. This book combines hardware principles with practical communication system design. Different communication systems are emerging due to different applications requirements in terms of performance, power, form factor and cost. Moreover, this book provides a comprehensive perspective on emerging 5G mobile networks. It identifies its architecture and its key enabling technologies such as M-MIMO, beamforming, mmWaves, machine learning and network slicing. Moreover, this book explores the evolution of wireless mobile networks over the next ten years towards 5G and beyond. It discusses 5G and its use-cases, system requirements, challenges and opportunities. Fremont, USA

Khaled Salah Mohamed

vii

Contents

Wireless Communication Systems: Foundation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Milestones in Communications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Frequency Spectrum: Frequencies for Communication . . . . . . . . . . . . . . . . . . . . . 2.1 Radio Radiation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Microwave Radiation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Infrared Radiation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Visible Light Radiation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5 Ultra-Violet Radiation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6 X-Rays Radiations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.7 Alpha Particles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.8 Beta Particles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.9 Gamma-Rays Radiation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.10 Neutron Radiation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Wireless Communication Systems Classification . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Efficiency-Based Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Coverage-Based Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Wireless Communication System Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Information Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Bandwidth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Spectral Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 Sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4 Nyquist Sampling Theorem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5 Shannon Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.6 Edholm’s Law . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.7 Wireless Radio Channel: Transmission Impairments . . . . . . . . . . . . . . . . . 6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 1 2 4 8 9 9 9 10 10 10 11 12 12 12 12 14 17 17 19 19 20 21 21 22 24 25

ix

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Contents

Wireless Communication Systems: Compression and Decompression Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Lossy and Lossless Compression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Compression Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Text Compression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Image Compression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Video Compression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Audio Compression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Performance Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Compression Ratio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Processing/Compression Time and Speed . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Comparative Study for All Different Compression Techniques . . . . . . . . . . . . . . 5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

27 27 28 29 34 42 46 49 49 49 49 51 51

Wireless Communication Systems: Confidentiality . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Symmetric Encryption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Stream Ciphers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Block Ciphers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Asymmetric Encryption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 AES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Hybrid Encryption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Authentication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Crypto Analysis/Attacks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Exhaustive/Brute-Force Attack . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Statistical/Histogram Attack . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 Differential Attack . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4 Known/Chosen Plaintext/Ciphertext Attack . . . . . . . . . . . . . . . . . . . . . . . . . 5.5 Man-in-the-Middle Attack . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Secured Wireless Communication System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1 AES Security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 5G Security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

55 55 56 56 58 59 60 60 61 62 62 63 63 64 64 65 65 66 66

Wireless Communication Systems: Reliability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Error Detection Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Checksum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Parity Check . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 CRC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Error Correction Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

69 69 70 70 70 70 73

Contents

3.1 Backward Error Correction Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Forward Error Correction Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Channel Equalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Linear Equalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Adaptive Equalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Diversity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Space Diversity or Antenna Diversity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Time Diversity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 Code Diversity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4 Polarization Diversity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5 Frequency Diversity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wireless Communication Systems: Line Coding, Modulation, Multiple Access, and Duplexing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Why Modulation? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Types of Modulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Types of Pulse Modulation: Analog to Digital Up-Conversion . . . . . . . . 2.2 Types of Analog Modulation: Analog to Analog Up-Conversion . . . . . . 2.3 Types of Encoding Modulation/Line Coding: Digital to Digital Up-Conversion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Types of Digital Modulation: Digital to Analog Up-Conversion . . . . . . . 2.5 Adaptive Modulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6 Types of Digital Demodulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.7 Factors Affecting Choice of Modulation . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.8 Multiple Access and Spread Spectrum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.9 Duplexing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.10 Polarization Reuse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wireless Communication Systems: Standards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Wireless Body Area Network (WBAN) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 PAN: Bluetooth Transceiver . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 LAN: WiFi Transceiver . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 WAN: 4G/LTE Transceiver . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 BN: DVB-S Transceiver . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 GN: GPS Transceiver . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 A Comparative Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

xi

73 75 92 93 93 94 95 95 95 95 95 95 96 101 101 101 102 104 105 109 120 121 121 122 130 131 131 131 133 133 134 135 136 137 138 139 141

xii

Contents

5G Mobile Communications: Fundamentals, Key Enabling Technologies, Challenges, Opportunities, Future Trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Cellular System Infrastructure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Evolution of Wireless Mobile Communications . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 5G KPIs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 5G Opportunities/Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 5G Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 5G Momentum: 5G Key Wireless Enabling Technologies . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

143 143 144 151 153 155 155 163

Paving the Way Towards 6G . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 6G Opportunities/Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 6G Potential Key Wireless Emerging Enabling Technologies . . . . . . . . . . . . . . . 3 6G KPIs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 6G Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

165 165 167 180 180 181 182

Wireless Communication Systems: Foundation

In this chapter the principles to the digital wireless communication systems is introduced. A digital wireless communication system consists of transmitter, channel and receiver. Moreover, it covers the wireless communication foundations, reviews the history of it, and introduces the applications, requirements and key technical features. The digital wireless signal processing such as: modulation, demodulation, orthogonal frequency division multiple access and error correction are discussed. Wireless communications provides mobility (on the go), flexibility (any place, any time, temporary, permanent), no problems with wiring also cost reducing, robust against disasters like earthquake, fire. Wireless communication is no longer a luxury but a necessity. Wireless Communication Systems also provide different services/technologies like video conferencing, cellular telephone, paging, TV, Radio etc. Speed and mobility for different wireless communication systems.

1

Milestones in Communications

In the early stage of human life, many used light, fire, flags, and pigeon for wireless communication. In modern life, invention of telegraph by Marconi was the first important milestone, where there is one switch to send long and short impulses at sender (Fig. 1). The most important application of RF energy is in providing telecommunication services. These applications include radio and television broadcasting, cellular telephony, personal communication services, cordless telephones, business radio, radio communications for the police, amateur radio, microwave point-to-point links and satellite communications. The main milestone in communications are summarized in Table 1. Mainly, there are three categories of: wireless (mobile), wireless without mobility and wired networks. World War II provided a significant push for the development of mobile and portable radio

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 K. S. Mohamed, Wireless Communications Systems Architecture, Synthesis Lectures on Engineering, Science, and Technology, https://doi.org/10.1007/978-3-031-19297-5_1

1

2

Wireless Communication Systems: Foundation

Fig. 1 Telegraph. The wireless communication concept came into existence when in 1928 Nyquist laid down the theory of telegraph transmission

systems, including two—way systems known as “walkie talkies” that could be carried in the field and are considered as the distant ancestors of today’s cell phones.

2

Frequency Spectrum: Frequencies for Communication

Electromagnetic radiation comes from the sun and transmitted in waves or particles at different wavelengths and frequencies. This broad range of wavelengths is known as the electromagnetic (EM) spectrum. Electromagnetic waves consist of oscillating electric and magnetic fields, whose frequency (f) and wavelength (λ) are related as follows: c = fλ, where c is the speed of light, approximately 3 × 105 m/s. The spectrum is generally divided into seven regions in order of decreasing wavelength and increasing energy and frequency. The common designations are radio waves, microwaves, infrared (IR), visible light, ultraviolet (UV), X-rays and gamma-rays. The frequency range and its allocation is shown in Fig. 2 and Table 2. Electromagnetic waves are waves which can travel through

2

Frequency Spectrum: Frequencies for Communication

3

Table 1 The main milestone in communications Year

Milestone

What to send?

1835

Telegraph (Morse code)

Letters

1876

Telephone (Bell)

Audio

1880

Radio (Marconi)

Audio

1936

Television

Video

1948

Information theory (Shannon)

All

1955

Fax

Data

1957

Satellite

Data

1975

Fiber optics

Data

1989

The internet (WWW)

All

1992

Mobile

All

2010

4G

All

2020

5G

All

the vacuum of outer space. Mechanical waves, unlike electromagnetic waves, require the presence of a material medium in order to transport their energy from one location to another. Sound waves are examples of mechanical waves while light waves are examples of electromagnetic waves. Electromagnetic waves are created by the vibration of an electric charge. This vibration creates a wave which has both an electric and a magnetic component. Despite Electromagnetic radiation comes from the sun naturally, there are artificial methods to generate electromagnetic radiation such as antennas as will be discussed later (Fig. 3). In many applications, electromagnetic waves are generated and travel through free space at the speed of light. Radio waves and microwaves emitted by transmitting antennas are one form of electromagnetic energy. This electromagnetic energy is characterized by its frequency (in Hz) and wavelength. Radio frequency waves occupy the frequency range 3 kHz to 300 GHz. Microwaves are a specific category of radio waves that cover the frequency range 1 GHz to approximately 100 GHz. RF and microwave radiation is non-ionizing because the energy levels associated with it are not high enough to cause ionization of atoms and molecules. Non-ionizing radiation does not have enough energy to knock electrons out of atoms. Near ultraviolet, visible light, infrared, microwave, radio waves, and low-frequency radio frequency (longwave) are all examples of non-ionizing radiation. By contrast, far ultraviolet light, X-rays, gamma-rays, and all particle radiation from radioactive decay are ionizing. A Summary of Radiation types and how to be generated are shown in Table 3. Moreover, the spectrum can be divided into licensed and unlicensed spectrum. The unlicensed band is called the industrial, scientific and medical (ISM) band.

4

Wireless Communication Systems: Foundation

Fig. 2 The frequency range (logarithmic scale)

2.1

Radio Radiation

Communications can be wired or wireless. In wireless communication, the transmission and reception of signals is accomplished with Antenna which are electrical devices that transform the electrical signals to radio signals in the form of Electromagnetic (EM) Waves and vice versa. These Electromagnetic Waves propagates through space. Electromagnetic Waves carry the electromagnetic energy of electromagnetic field through space. Electromagnetic Waves includes Gamma Rays, X-Rays, Ultraviolet Rays, Visible Light, Infrared Rays, Microwave Rays and Radio Waves. For radio/microwave wave, the electric component of the wave results from the voltage changes that occur as the antenna element

2

Frequency Spectrum: Frequencies for Communication

5

Table 2 Electromagnetic spectrum range Class

Abbreviation

Range

Applications

Extremely low frequency

ELF

30–300 Hz

Power lines

Very low frequency

VLF

3–30 kHz

Home security

Notes

Kilobyte = 103

Low frequency

LF

30–300 kHz

–

Medium frequency

MF

300–3000 kHz

AM radio

High frequency

HF

3–30 MHz

SSB radio (used by boats)

Very high frequency

VHF

30–300 MHz

TV, FM radio

Ultra high frequency

UHF

300–3000 MHz

Mobiles, TV

Super high frequency

SHF

3–30 GHz

Satellite

Extremely high frequency

EHF

30–300 GHz

Radar, microwave

Infrared radiation

–

10–100 THz

Medical applications (relief of muscle pain and tension)

Visible light

–

400–750 THz

Light blubs

Ultra-violet radiation

–

1–100 PHz

Industrial (kill bacteria Petabyte = 1015 in food)

X-rays radiation –

0.1–100 EHz

Medical applications (see Broken bones)

Exabyte = 1018

Gamma-rays radiation

0.1–1000 ZHz

Nuclear industry, medical applications (radiotherapy)

Zettabyte = 1021

–

Megabyte = 106

Gigabyte = 109

Terabyte = 1012

is excited by the alternating waveform. The lines of force in the electric field run along the same axis as the antenna, but spreading out as they move away from it. This electric field is measured in terms of the change of potential over a given distance, e.g. volts per meter, and this is known as the field strength. This measure is often used in measuring the intensity of an electromagnetic wave at a particular point. The magnetic field is at right angles to the electric field and hence it is at right angles to the plane of the antenna (Fig. 4). It is generated as a result of the current flow in the antenna. The radio channel of a wireless communication system is often described as being either LOS or NLOS [1]. In a LOS link, a signal travels over a direct and unobstructed path from the transmitter

6

Wireless Communication Systems: Foundation

EM wave Generation

Artificial

Natural

Sun

Bodies

Antennas

LEDS

Fig. 3 EM wave generation

Table 3 A summary of radiation types and how to be generated Radiation type

How to be artificially generated?

Radio

Antenna

Microwave

Magnetron

IR/visible light/UV

LED

X-ray

Tungsten cathode and anode encased in a Pyrex glass vacuum tube

Gamma-ray

Collision with another particle at high velocity

to the receiver. A LOS link requires that most of the first Fresnel zone is free of any obstruction. The Fresnel clearance required depends on the operating frequency and the distance between the transmitter and receiver locations. In a NLOS link, a signal reaches the receiver through reflections, scattering, and diffractions. The signals arriving at the receiver consists of components from the direct path, multiple reflected paths, scattered energy, and diffracted propagation paths. These signals have different delay spreads, attenuation, polarizations, and stability relative to the direct path. The multi path phenomena can also cause the polarization of the signal to be changed. Thus using polarization as a means of frequency re-use, as is normally done in LOS deployments can be problematic in NLOS applications. How a radio system uses these multi path signals to an advantage is the key to providing service in NLOS conditions. A product that merely increases power to penetrate obstructions (sometimes called “near line of sight”) is not NLOS technology because this approach still relies on a strong direct path without using energy present in

2

Frequency Spectrum: Frequencies for Communication

7

Fig. 4 Electromagnetic wave

Fig. 5 The atmosphere layers

the indirect signals. Both LOS and NLOS coverage conditions are governed by the propagation characteristics of their environment, path loss, and radio link budget. The layers of atmosphere can be summarized in Fig. 5 [2]. The mode of propagation of electromagnetic waves in the atmosphere and in free space may be divided in to the following three categories (Fig. 6):

2.1.1

Ground Wave Propagation

Ground wave propagation of the wave follows the contour of earth. At Medium Wave frequencies (300 kHz to 3 MHz) and in the lower HF bands (3–30 MHz), aerials tend

8

Wireless Communication Systems: Foundation

Surface Wave 30 MHz

Fig. 6 Electromagnetic waves propagations

to be close to the ground (in terms of wavelength). Hence the direct wave and reflected wave tend to cancel each other out (there is a 180° phase shift on reflection). This means that only the surface wave remains. A surface wave travels along the surface of the earth by virtue of inducing currents in the earth. The surface waves die more quickly as the frequency increases. It can be characterized by the below equation Ground Wave = Direct Wave + Reflected Wave + Surface Wave

2.1.2

(1)

Sky Wave Propagation

When the wave has to travel a longer distance. Here the wave is projected onto the sky and it is again reflected back onto the earth. Signal reflected from ionized layer of atmosphere back down to earth.

2.1.3

Line of Sight (LOS) Propagation

It travels to the distance up to which a naked eye can see. Line-of-Sight propagation is a characteristic of electromagnetic radiation or acoustic wave propagation which means waves travel in a direct path from the source to the receiver.

2.2

Microwave Radiation

Microwaves are produced inside the oven by an electron tube called a magnetron. The microwaves are reflected within the metal interior of the oven where they are absorbed by food. Microwaves cause water molecules in food to vibrate, producing heat that cooks the

2

Frequency Spectrum: Frequencies for Communication

9

food. That’s why foods that are high in water content, like fresh vegetables, can be cooked more quickly than other foods. The microwave energy is changed to heat as it is absorbed by food, and does not make food “radioactive” or “contaminated”. The magnetron is a high-powered vacuum tube that works as a self-excited microwave oscillator. Crossed electron and magnetic fields are used in the magnetron to produce the high-power output required in radar equipment. These multi-cavity devices may be used in radar transmitters as either pulsed or CW oscillators [3]. Terahertz radiation (T-Ray) is the next level. It is just about higher frequency.

2.3

Infrared Radiation

A light-emitting diode (LED) is a semiconductor light source that emits light when current flows through it. Electrons in the semiconductor recombine with electron holes, releasing energy in the form of photons. The color of the light (corresponding to the energy of the photons) is determined by the energy required for electrons to cross the band gap of the semiconductor. Infrared LEDs are used in remote-control circuits. Modern LEDs are available across the visible, ultraviolet (UV), and infrared wavelengths, with high light output. Infrared radiation has many applications such as night vision. For night vision, digital cameras can see some infrared with their sensors. Unlike human eye, sensors have a wider frequency range. So IR emitting diodes will be registered by the sensor, even though they are invisible by the human eye.

2.4

Visible Light Radiation

All electromagnetic radiation is light, but we can only see a small portion of this radiation—the portion we call visible light. When you turn on a light bulb, electricity flows through the filament. As the filament heats up it produces light. The color of the light depends on the temperature of the filament.

2.5

Ultra-Violet Radiation

One of the most common ways of producing UV light is passing an electric current through vaporized mercury or some other gas. This type of lamp is commonly used in tanning booths and for disinfecting surfaces. The lamps are also used in black lights that cause fluorescent paints and dyes to glow. Light-emitting diodes (LEDs), lasers and arc lamps are also available as UV sources with various wavelengths for industrial, medical (premature baby) and research applications [4]. For example, premature babies are put under UV light to treat newborn jaundice by lowering the bilirubin levels in the

10

Wireless Communication Systems: Foundation

baby’s blood through a process called photo-oxidation. Photo-oxidation adds oxygen to the bilirubin so it dissolves easily in water [5].

2.6

X-Rays Radiations

The generation of X-rays requires a Tungsten cathode and anode encased in a Pyrex glass vacuum tube. The cathode filament is used to generate electrons via thermionic emission and the anode is used as the target for the accelerated electrons. A step-up and a stepdown transformer is used to transform the regular 110 V alternating current to a high voltage at the level of the tube (more than 50 kV), and to a low voltage at the level of the filament (10 V), respectively. A focusing cup houses the cathode and helps in preventing the electrons from repelling each other away from the ligament and allows the stream of electrons from cathode to anode to be controlled. A dead-man switch timer is connected to the circuit and controls the time the electrons travel from the filament to the target (Fig. 7). When the X-ray tube is turned on, the low voltage circuit is activated to preheat the filament to a specific temperature to generate electrons through thermionic emission. The number of electrons generated is proportional to the temperature of the filament. When the timer is activated, the high voltage circuit is also activated and the electrons at the filament will start travelling at high speed towards the target. The higher is the voltage between the cathode and the anode the faster will the electrons travel. The number of electrons travelling from the cathode to the anode is called the tube current. The maximum energy output of the X-ray tube is called the kilo volt peak.

2.7

Alpha Particles

Alpha particles are charged particles, which are emitted from naturally occurring materials (such as uranium, thorium, and radium) and man-made elements (such as plutonium and americium). These alpha emitters are primarily used (in very small amounts) in items such as smoke detectors. In general, alpha particles have a very limited ability to penetrate other materials. In other words, these particles of ionizing radiation can be blocked by a sheet of paper, skin, or even a few inches of air. Nonetheless, materials that emit alpha particles are potentially dangerous if they are inhaled or swallowed, but external exposure generally does not pose a danger.

2.8

Beta Particles

Beta particles, which are similar to electrons, are emitted from naturally occurring materials (such as strontium-90). Such beta emitters are used in medical applications, such as

2

Frequency Spectrum: Frequencies for Communication

11

Fig. 7 Schematics of an X-ray tube [6]

treating eye disease. In general, beta particles are lighter than alpha particles, and they generally have a greater ability to penetrate other materials. As a result, these particles can travel a few feet in the air, and can penetrate skin. Nonetheless, a thin sheet of metal or plastic or a block of wood can stop beta particles.

2.9

Gamma-Rays Radiation

Gamma rays are the result of an excited nucleus de-exciting to a lower energy state, in some ways analogous to the production of characteristic X-rays by the de-excitation of orbital electrons in an atom. Excited nuclear states may arise from any stimulus that imparts sufficient energy to the nucleus, such as (1) the collision with another particle at high velocity, (2) the capture of another nucleon in a nuclear reaction, or (3) creation of a new nucleus by radioactive decay. Computer simulations show that blasting plastic with strong laser pulses could produce gamma rays with unprecedented intensity, good for fundamental physics experiments and possibly cancer treatments [7].

12

Wireless Communication Systems: Foundation

Fig. 8 Penetration ability of neutron radiation [8]

2.10

Neutron Radiation

Neutron radiation is a form of ionizing radiation that presents as free neutrons. Typical phenomena are nuclear fission or nuclear fusion causing the release of free neutrons. It has an exceptional ability to penetrate other materials (Fig. 8).

3

Wireless Communication Systems Classification

3.1

Efficiency-Based Classification

Communications systems can be classified according to efficiencies as follows, noting that the design of wireless communication systems implies a trade-off between these categories [9]: • • • • •

Bandwidth efficiency: radio spectrum utilization. Bit-error rate (BER) efficiency: noise immunity. Power efficiency: link budget, selection of the amplifiers and the number of antennas. Cost efficiency: low hardware and software resources in transmitters and receivers. Coverage efficiency: the range that can be covered.

3.2

Coverage-Based Classification

Different communication systems classifications according to coverage range are shown in Fig. 9. Wireless Communication Systems also provide different services/technologies like video conferencing, cellular telephone, paging, TV, Radio etc. Speed and mobility for different wireless communication systems are shown in Fig. 10. Wireless communication includes a wide range of network types and sizes (Table 4). The classification includes:

3 Wireless Communication Systems Classification

Global Network (GN)

Broadcast Network (BN)

13

Mobile Satellite Services GPS

Digital Audio Broadcast Digital Video Broadcast Mobile TV

4G/5g Cellular

Wide Area Network (WAN) Wireless LAN: WiFi

Local Area Network (LAN)

Personal Area Network (PAN)

Body Area Network (BAN)

Bluetooth, UWB, RFID, NFC

Wearables

Fig. 9 Wireless communication systems classifications. Power is the main factor affecting the communication system

Fig. 10 Speed and mobility for different wireless communication systems/technologies. Bluetooth operates in the unlicensed spectrum while 4G operates in the licensed spectrum

14

Wireless Communication Systems: Foundation

Table 4 Different wireless networks and its coverage Distance

Coverage

Network

1m

Rooms

BAN, PAN

10 m

Rooms

LAN

100 m

Buildings

1 km

Campuses

10 km

Cities

WAN

100 km

Countries (national)

WAN, GN, GN

1000 km

Continents (international)

≥ 10,000 km

Planets

• • • • •

Global network (GN): such as GPS and mobile satellite services. Broadcast network (BN): such as digital video/audio broadcasting and mobile TV. Wide area network (WAN): such as 4G/5G cellular. Local area network (LAN): such as WiFi. Personal area network (PAN): A PAN technology provides communication over a short distance such as Bluetooth. • Body area network (BAN): such as wireless wearables [10].

4

Wireless Communication System Architecture

Wireless communication systems are becoming more complex every day. The most recent wireless communication systems proposed is 5G. Communication systems typically need to perform several tasks to successfully transmit and receive signals over a communication channel. They, therefore, consist of a number of subsystems, or modules, ranging from Analog Digital Converters (ADCs)/Digital Analog Converters (DACs) to filtering, modulation, encoding and decoding modules, etc. A typical Wireless Communication System can be divided into three elements: the Transmitter, the Channel and the Receiver. The Transmitter converts the electrical signal into a form that is suitable for transmission. The Receiver: to recover the message contained in the corrupted received signal. A transceiver is a combination transmitter/receiver in a single package. A wireless transceiver consists of two functional layers: a physical (PHY) layer and a media access control (MAC) layer. The PHY layer consists of an RF front end and a baseband layer. The baseband layer of (Tx side) consists of [21–34]: • Source encoder: Compression • Encryption

4 Wireless Communication System Architecture

15

• Channel encoder: Error Correction • Modulation The baseband layer of (Rx side) consists of: • • • •

Source decoder: Compression to remove redundancy decryption Channel decoder: Error Correction Demodulation

The wireless transceiver RF front end adds an RF carrier to the baseband symbol stream for transmission (up-convert) and down-converts the received RF signal to baseband. Moreover, the radio front end has power and low noise amplifiers with variable gain for transmitter and receiver, respectively. The two quadrature mixers are used to up-covert the baseband signal to RF and vice versa. The MAC layer provides link traffic control for the wireless transmitter to access the wireless links, avoid collisions, and optimize data throughput. The MAC layer acts as a finite state machine. Based on the status of the channel, the received signal frames, and the frames to be transmitted, the MAC determines what and when to transmit next [11]. The medium access control protocol schedules transmissions on the air interface and controls the low-level operation of the physical layer. A High-Level communication system architecture: Tx side, noisy channel, Rx Side is shown in Fig. 11. A detailed block diagram for the PHY layer is shown in Fig. 12 and a block diagram for the baseband layer and RF frontend is shown in Figs. 13 and 14 respectively. The detailed components are summarized in Table 5.

Wireless Transceiver PHY layer

RF Layer

Baseband Layer

Fig. 11 Wireless communication system: the big picture

MAC layer

16

Wireless Communication Systems: Foundation

From other sources 8B10B Data bus inversion

TX: Information Source Text Image Audio Video

Format

Channel Encoder

Source Encoder (Compression)

Encrypt Error Correction + Interleaver (Scrambler) + Delimiter

(All in digital format) A/D for Audio

Line/ Symbol Encoder

Mux

Modulation (Small antenna)

(For clock recovery)

Spread Spectrum (EMI) + Multiple access

• Coaxial Cable • EM wave (Radio) • Fiber optics

Duplexer+ RF + LNA+ Filter + + Frequency Down converter + ADC

Rx: Text Image Audio Video

De-format D/A –if needed-

Source decoder Decompression

Duplexer+ RF + Power Amplifier + Filter + Frequency Up converter + DAC

Noisy Channel

Channel decoder Decrypt Error Correction + Deinterleaver

Analog/RF

Mixed

Demux

Line/ symbol Decoder

Demodulation

De-spread Spectrum + Multiple access

Digital

Fig. 12 A detailed block diagram of a digital communication system (PHY layer): Tx side, noisy channel, Rx side (RF front end + baseband layer)

Fig. 13 High-level communication system baseband layer: Tx side, noisy channel, Rx side

5

Information Theory

17

Fig. 14 High-level communication system RF layer

5

Information Theory

Information theory is a subfield of mathematics concerned with transmitting data across a noisy channel.

5.1

Bandwidth

Spectrum of a signal is the range of frequencies contained in the signal. Absolute bandwidth of a signal is the width of the signal spectrum. Effective bandwidth of a signal is the narrow band of frequencies containing “most” of the signal energy. Data Rate/Throughput The maximum data rate limit (C) over a medium is decided by following factors: • Bandwidth of channel (W). • Signal levels (P). • Channel quality/level of noise (N). Two theoretical formulas were developed to calculate the data rate: • For a noiseless channel: Nyquist bit rate • For noiseless channel: Shannon capacity The Shannon capacity gives us the upper limit; the Nyquist formula tells us how many signal levels we need.

18

Wireless Communication Systems: Foundation

Table 5 Different components of any wireless communication system Tx side Format

Analog to digital. For example: an analog voice signal is digitized in several steps: in the first step, the bandwidth of the input signal is limited to 300–3400 Hz to enable the signal with the limited bandwidth of a 64-kbit/s timeslot to be carried. Afterward, the signal is sampled at a rate of 8000 times per second. The next step in the processing is the quantization of the samples, which means that the analog samples are converted into 8-bit digital values that can each have a value from 0 to 255

Source encoder: compression

Data compression to remove redundancy

Encrypt

Cipher data for protection

Channel encoder: error correction Add redundancy for error detection and correction at receiver. Moreover, it can be combined with interleaver to reduce burst errors Multiplexing

Accommodate several simultaneous transmissions, i.e. combine several low-rate signals to a high-rate one

Line encoder/pulse shaping

How bits are transmitted and how to limit transmit signal bandwidth

Modulation

Modulate baseband signal by carrier frequency for radio transmission (long distance transmission

RF components

Digital-to-analog converter (DAC): takes digital signal values and outputs analog signal i.e. current/voltage on the circuit Low pass filter (LPF): removes high frequency signals Mixer/PLL: Move signal to desired frequency of operation Band pass filter (BPF): removes Frequency Signals outside desired frequency of operation Power amplifier: amplify signal power

The channel is a medium, such as wire, coaxial cable, a waveguide, an optical fiber or a radio link (air interface) Rx side Deformat

Recover original digital data, digital to analogue

Source decoder: decompression

Data decompression

Decrypt

Decipher data

Channel decoder: error correction Detect and correct errors in bits, which can be hard, or soft, or iterative De-multiplexing

Accommodate several simultaneous transmissions

Line decoder

Compensate channel distortion (ISI) (continued)

5

Information Theory

19

Table 5 (continued) Demodulation

Carrier recovery and remove carrier, i.e. passband signal → baseband one

RF components

Low noise amplifier (LNA): amplify signal power with minimal noise Analog-to-digital converter (ADC): takes analog signal and digitizes it, i.e. samples and quantizes the signal values

5.2

Spectral Efficiency

The information rate that can be transmitted over the given bandwidth in a specific. Spectral efficiency quantifies transmitted bitrates (b/s) per 1 Hz band and is given by: ( ) T hr oughput bs Spectral efficiency = (1) Bandwidth(Hz)

5.3

Sampling

Many signals originate as continuous-time signals (y(t)), e.g. conventional music or voice. By sampling a continuous-time signal at isolated, equally-spaced points in time, we obtain a sequence of numbers (ys (t)). An example is depicted in Fig. 15. Fig. 15 Sampling

y (t)

t

yS(t)

t

20

Wireless Communication Systems: Foundation

5.4

Nyquist Sampling Theorem

A bandlimited continuous-time signal can be sampled and perfectly reconstructed from its samples if the waveform is sampled (f s ) over twice as fast as its highest frequency component (f max ). An example is depicted in Fig. 16. N yquist rate : f s ≥ f max

(2)

C = 2W log2 M

(3)

where C M W

The maximum data rate limit, The number of signed levels used to present the analog signal, The capacity of a Noiseless Channel.

For 2-levels encoding, C = 2W. For 4-levels encoding, C = 4W.

Original signal

Fig. 16 Sampled versus original signal

Sampled signal

5

Information Theory

5.5

21

Shannon Theory

Every communication channel had a speed limit (capacity), measured in binary digits per second. ) ( S (4) C = W log2 1 + N where [12], C W S N

The maximum data rate limit, The capacity of a White Gaussian Noise Channel, Signal levels, Channel quality/level of noise, is called signal to noise ratio.

S N

From the above equation we can notice that, for better power efficiency, lower data rate per unit BW is required. So, there is a tradeoff between power efficiency and bandwidth efficiency. For small SNR values the logarithmic part approximates as: ( ) S S C = W log2 1 + =W (5) N N And for high SNRs it approximates: ) ( ( ) S S = W log2 C = W log2 1 + N N

(6)

The entropy of a random variable is the average level of information or uncertainty inherent in the variable’s possible outcomes. It quantifies how much information there is in a message.

5.6

Edholm’s Law

Edholm’s law predicts that the bandwidth and data rates double every 18 months, which has proven to be true since the 1970s. It is like Moore’s law, but the latter is for number of transistors [13].

22

Wireless Communication Systems: Foundation

Fig. 17 An illustrative example to show the difference between reflection, diffraction, scattering

5.7

Wireless Radio Channel: Transmission Impairments

Fresnel Zones are ellipse shaped areas between any two radios. The primary Fresnel zone is required to be at least 60% clear of any obstruction to ensure the highest performance of wireless link [14–16].

5.7.1

Reflection, Diffraction, Scattering

• Reflection: Surface large relative to wavelength of signal. • Diffraction: Edge of impenetrable body that is large relative to wavelength of signal. • Scattering: Obstacle size on order of wavelength such as Lamp or raindrops (Fig. 17).

5.7.2

Attenuation/Path Loss

Attenuation is falling off signal strength with distance it depends on the medium. For guided media (cables), the attenuation is generally exponential and thus is typically expressed as a constant number of decibels per unit distance. For unguided media (the atmosphere), attenuation is a more complex. Attenuation can be overcome by using amplifiers and repeaters. Attenuation in free space increases with frequency. This is known as Frii’s Law. ) ( λ 2 (7) PR = PT G T G R 4π d where

5

Information Theory

PR PT GT GR d

5.7.3

is is is is is

the the the the the

23

power at receiver side, transmitter power, transmitter antenna gain, receiver antenna gain, distance between transmitter and receiver.

Atmospheric Losses

Radio waves are absorbed by molecules of oxygen and water vapor at frequencies that change their rotational motion.

5.7.4

Delay Distortion: Inter Symbol Interference (ISI)

Delay distortion occurs because the velocity of propagation of a signal through a guided medium varies with frequency. Various frequency components of a signal will arrive at the receiver at different times, resulting in phase shifts between the different frequencies. Delay distortion is particularly critical for digital data. Some of the signal components of one bit position will spill over into other bit positions, causing inter symbol interference (ISI), which is a major limitation to maximum bit rate over a transmission channel. ISI means that symbols arriving along late paths interfere with following symbols. Pulse shaping causes ISI and it can be solved by channel equalization.

5.7.5

Multi-path Rayleigh and Rician Fading

Radio transmitter sends signals in all directions. Signals bounce off various objects, arrive at destination through many alternate paths. At object surfaces, reflection, diffraction or scattering can happen. Multi-path may add constructively or destructively. The multi path phenomena can also cause the polarization of the signal to be changed. As an electromagnetic wave propagates, the tip of the time-varying electric field vector at any point in space traces an ellipse. The shape of this ellipse can vary from linear to circular and this is referred to as the polarization of the wave. Linearly polarized waves are referred to by angle, e.g., vertical, horizontal, or 45°. Elliptically or circularly polarized waves have a polarization sense referred to as “left-hand” or “right hand” depending on the direction of rotation of the field vector with time. Rayleigh fading is sometimes considered a special case of Rician fading for when there is no line of sight signal. Rician fading is a stochastic model for radio propagation caused by partial cancellation of a radio signal by itself. It occurs when one of the paths, typically a line of sight signal or some strong reflection signals, is much stronger than the others [17, 18].

5.7.6

Noise

For any data transmission event, the received signal will consist of the transmitted signal, modified by the various distortions imposed by the transmission system, plus additional

24

Wireless Communication Systems: Foundation

unwanted signals (noise) that are inserted somewhere between transmission and reception. There are four categories of noise: • Thermal/white noise: Due to thermal agitation of electrons. It is present in all electronic devices and transmission media, and is a function of temperature. White means that the thermal noise involves a constant power spectral density with respect to frequency. Also, it also called AWGN which is a noise that affects the transmitted signal when it passes through the channel. It contains a uniform continuous frequency spectrum over a particular frequency band. • Intermodulation noise: the mixing of signals at f1 and f2 might produce energy at frequency f1 + f2 . This derived signal could interfere with an intended signal at the frequency f1 + f2 . • Crosstalk: It is an unwanted coupling between signal paths. It can occur by electrical coupling between nearby twisted pairs. • Impulse noise: It is generated from a variety of cause, e.g., external electromagnetic disturbances such as lightning.

5.7.7

Doppler Shift: Frequency Dispersion

Doppler shift occurs when the transmitter of a signal is moving in relation to the receiver. The relative movement shifts the frequency of the signal, making it different at the receiver than at the transmitter. In other words, the frequency perceived by the receiver differs from the one that was originally emitted [19]. If the transmitter or receiver or both are mobile, so the frequency of received signal changes. If they are moving towards each other, then the frequency increases. If they are moving away from each other, then the frequency decreases. Frequency difference can be found by the following formula [20, 21]: Frequency difference =

V elocit y V elocit y × Fr equency = W avelength Speedo f light

(8)

Figure 18 shows time dispersion versus frequency dispersion. Fast fading requires short packet durations, thus high bit rates. Multipath delay spreads require long symbol times.

6

Conclusions

This chapter is an introduction to the digital communication systems. It presents the principle and metrics of the digital communication systems with its different parts: transmitter, channel and receiver. Moreover, this chapter covers the wireless communication foundations, reviews the history of it, and introduces the applications, requirements and

References

25

Time Dispersion

Time Domain Interpretation

Frequency Domain Interpretation

Fast Fading

Doppler Effect

Frequency Dispersion

Inter Symbol Interference

Frequency selective fading

Fig. 18 Time dispersion versus frequency dispersion

key technical features of wireless communication. In addition, it reviews the DSP techniques used for wireless communication such as: modulation, demodulation, orthogonal frequency division multiple access and error correction.

References 1. R. Mendrzik et al., Harnessing NLOS components for position and orientation estimation in 5G millimeter wave MIMO. IEEE Trans. Wirel. Commun. 18(1), 93–107 (2019). ISSN: 1536-1276 2. J.R. Smith, Modern Communication Circuits, 2nd edn. (McGraw Hill, New York, 1998) 3. https://www.radartutorial.eu/08.transmitters/Magnetron.en.html 4. https://www.livescience.com/50326-what-is-ultraviolet-light.html 5. https://www.nhs.uk/conditions/jaundice-newborn/treatment/ 6. https://www.dentalcare.com/en-us/professional-education/ce-courses/ce570/what-is-neededto-generate-x-rays 7. https://physics.aps.org/articles/v9/50 8. R. Steele, L. Hanzo (eds.), Mobile Radio Communications: Second and Third Generation Cellular and WATM Systems, 2nd edn. (Wiley, Chichester, 1999) 9. F. Xiong, Digital Modulation Techniques (Artech House, Norwood, 2006) 10. Y. Qu, G. Zheng, H. Ma, X. Wang, B. Ji, H. Wu, A survey of routing protocols in WBAN for healthcare applications. Sensors 19, 1638 (2019) 11. https://www.mathworks.com/discovery/wireless-transceiver.html?fbclid=IwAR106B-R3MTfJ OrN8nF8j-bQedDX2pSQC-PX9FA1MkzSaq57UCGHS9mOXus 12. C.E. Shannon, A mathematical theory of communication. Bell Syst. Tech. J. 27, 379–423 (1948) 13. S. Cherry, Edholm’s law of bandwidth. IEEE Spectr. 41(7), 58–60 (2004). https://doi.org/10. 1109/MSPEC.2004.1309810 14. S. Haykin, M. Moher, An Introduction to Analog and Digital Communications (Wiley, Hoboken, 2006) 15. B.P. Lathi, Z. Ding, Modern Digital and Analog Communication Systems, 4th edn. (Oxford University Press, Oxford, 2009) 16. J.G. Proakis, M. Salehi, Fundamentals of Communication Systems (Prentice Hall, Hoboken, 2004)

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17. A. Abdi, C. Tepedelenlioglu, M. Kaveh, G. Giannakis, On the estimation of the K parameter for the Rice fading distribution. IEEE Commun. Lett. 92 –94 (2001) 18. X. Leturc, P. Ciblat, C.J. Le Martret, Estimation of the Ricean K factor in the presence of shadowing. IEEE Commun. Lett. 24(1), 108–112 (2020) 19. https://www.nutaq.com/blog/doppler-shift-estimation-and-correction-wireless-communicatio ns-0 20. J. Geier, Wireless Networks First-Step (Cisco Press, Indianapolis, 2004), 264 pp. ISBN: 158720-111-9 (Safari Book) 21. S. Rackley, Wireless Networking Technology (Newnes, London, 2007), 416 pp. ISBN: 0-75066788-5 (Safari Book) 22. R.G. Gallager, Principles of Digital Communication (Cambridge University Press, Cambridge, 2008) 23. C.E. Shannon, A mathematical theory of communication. Bell Syst. Tech. J. (1948) 24. J.G. Proakis, M. Salehi, Communication Systems Engineering, 2nd edn. (Prentice Hall, Hoboken, 2002) 25. R.E. Ziemer, W.H. Tranter, Principles of Communications: Systems, Modulation and Noise, 6th edn. (Wiley, Hoboken, 2010) 26. B.P. Lathi, Z. Ding, Modern Digital Analog Communication Systems, 4th edn. (Oxford University Press, Oxford, 2009) 27. B. Sklar, Digital Communications: Fundamentals and Applications, 2nd edn. (Prentice-Hall, Hoboken, 2001) 28. S. Haykin, Communication Systems, 4th edn. (Wiley, Hoboken, 2001) 29. M.P. Fitz, Fundamentals of Communications Systems (McGraw Hill, New York, 2007) 30. L. Hanzo, W. Webb, T. Keller, Single- and Multi-Carrier Quadrature Amplitude Modulation: Principles and Applications for Personal Communications, WLANs and Broadcasting (Wiley, Chichester, 2000) 31. T.S. Rappaport, Wireless Communications: Principles and Practice (Prentice-Hall, Hoboken, 1996) 32. A. Paulraj, R. Nabar, D. Gore, Introduction to Space-Time Wireless Communications (Cambridge University Press, Cambridge, 2003) 33. D. Tse, P. Viswanath, Fundamentals of Wireless Communication (Cambridge University Press, Cambridge, 2005) 34. https://www.nrc.gov/about-nrc/radiation/health-effects/radiation-basics.html

Wireless Communication Systems: Compression and Decompression Algorithms

Nowadays, smart devices spreads around the world and internet of things (IoT) starts to include all kinds of data: text, image, audio, and video. Yet, limited bandwidth and storage centers had made limitations in sending and receiving the data. As a result, various data compression algorithms are being introduced from time to time to decrease the sizes of the data and facilitate sending them. Mainly, there are two types of compression: lossy and lossless compression. In this chapter, an overview of various data compression algorithms is presented for each data type, comparisons between different algorithms will be elaborated to show their best usage, and a conclusion will be made to show the differences between algorithms to show which one to be used. Data compressions has many applications. For example, data compression scheme is one that can be used to reduce transmitted data over wireless channels. Thus, reducing the main power consumer in wireless sensor networks. Data compression involves trade-offs among various factors, including the degree of compression, the amount of distortion introduced, and the computational resources required to compress and uncompressed data.

1

Lossy and Lossless Compression

Lossy compression removes some unimportant data. We cannot recover the original data. Shannon proposed the theory of lossy data compression which is known as rate-distortion theory. In lossy data compression, the decompressed data is not exactly the same as the original data, but some amount of distortion, D, is tolerated. He showed that, for a given source (with all its statistical properties known) and a given distortion measure, there is a function, R(D), called the rate-distortion function. The theory says that if D is the

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 K. S. Mohamed, Wireless Communications Systems Architecture, Synthesis Lectures on Engineering, Science, and Technology, https://doi.org/10.1007/978-3-031-19297-5_2

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tolerable amount of distortion, then R(D) is the best possible compression rate. When the compression is lossless, the best compression rate is R(0) = H. Hence, rate-distortion theory is a generalization of lossless data compression theory, where we go from no distortion (D = 0) to some distortion (D > 0). lossless compression makes it easy to recover the original image, yet it has low compression ratio, Shannon said that there is a limit to lossless data compression. This limit, called the entropy rate, is denoted by H. The value of H depends on the statistical nature of the source. It is possible to compress the source, in a lossless way, with compression rate close to H [1].

2

Compression Techniques

Multimedia compression focuses on reducing irrelevance and redundancy of the multimedia data where there are two categories of data compression, descriptively named lossless and lossy. The former follows a technique through which the size of the file is reduced without sacrificing its quality, ensuring that the original state is exactly restored. While the latter -our scope- uses irreversible inexact approximations and partial data discarding to represent the content, reducing data size for storing, handling, and transmitting content. Concepts for compression can be roughly categorized as shown in Fig. 1. Some techniques are dictionary based where a dictionary is filled on the run from data being compressed. Some algorithms depends on knowing the statistics of data being compressed or the probability of occurrence of chunks of data. Some algorithms are simply mathematical encoders with basic mathematical operations like division and remainder. Others simply stores the start and end of a run, where a run is a sequence with repeated data with no changes. Others are hybrid solutions that encapsulates more than one technique from the previously mentioned ones. Some other techniques are based on data prediction from data previously seen followed by encoding of the prediction error. The concepts are: • Concept 1: Start and End Locations of Repeated Patterns Simply, this concept stores the start and the end locations of sequences of data that have no changes. A sequence with no change is called a run. Run length encoding (RLE) is an algorithm that follows that concept. • Concept 2: Division and Remainder This concept applies mathematical encoding on each byte or chunk of data. This technique is commonly used with error encoding. Golomb-Rice (CR) is an example. • Concept 3: Dictionary Based Techniques These techniques fills a dictionary on the run from data being compressed. Dictionary is constructed during compression and reconstructed again during decompression without the need to store the dictionary. This obviously wouldn’t work with compression

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Fig. 1 Main concepts for compression

Dictionary-based (eg. Lempel-Ziv, 842B) Statististics and probability of occurrence (eg. Huffman encoding, Arithmetic encoding)

Concepts for compressions

Mathematical(division and remainder) and logical operations (eg. Golomb-Rice, Oring bits) Start and end locations of repeated patterns (Run Length encoding) Hybrid (eg. GZIP) Prediction and error encoding (eg. predictive encoding, autoencoders)

of small block sizes. This also adds the overhead of constructing and searching dictionaries. There is also a tradeoff between dictionary size and dictionary pointer size which is the actual stored data after compression. Lempel-Ziv (LZ) is an example. • Concept 4: Statistics and Probability of Occurrence These techniques are based on knowing the probability of occurrence or the ratio of occurrence of each item or byte of data being compressed. Huffman encoding is an example. • Concept 5: Prediction and Error Encoding This technique is based on data prediction from data previously seen followed by encoding of the prediction error. Predictive Encoding Technique is an example. • Concept 6: Hybrid Techniques These compression techniques encapsulates more than one technique cascaded to improve the performance. GZIP is an example.

2.1

Text Compression

Text is a very popular format used in data transmission. Text compression is done by transforming a large sized string of characters to another one with smaller size. There are many Compression algorithms nowadays. We are going to mention the most used ones:

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A. Huffman coding: lossless It is the oldest compression algorithm that is based on building a full binary tree based on the frequency of different symbols and characters in the original file [2]. Then, it takes the code-word of the symbol giving a very short code word to the most frequent symbol and relatively large code word to the least frequent one. The structure of the tree is given by combination of nodes step by step till the end of the root tree. There are two types of Huffman Coding: Static Huffman coding and Dynamic Huffman coding [3]: • Static Huffman coding assigns variable length code to symbols based on their frequency of occurrences in the original file, hence the file to be transmitted is first analyzed to know the relative frequency of characters, then the coding generates a binary tree with branches of 0 and 1, and finally the new code word of each character is sent to the receiver. • Dynamic Huffman coding is based on building the tree dynamically such that if the character to be transmitted is already in the tree, then its code-word is determined and sent normally, but if not, it has to be sent in its uncompressed form. Then the encoder updates the tree either by changing the frequency of occurrence or introducing a new character [4]. Huffman coding starts by assembling the symbols of the data to be compressed. A weight that represents the frequency of the symbol with respect to the data to be compressed is assigned to each symbol. It is based on the concept of mapping an alphabet to a different representation composed of strings of variable size such that symbols with a high probability of occurring have a smaller representation than those that occur less often. The method starts by building a list of all the alphabet symbols in descending order of their probabilities. It then constructs a tree, with a symbol at every leaf, from the bottom up. This is done in steps where at each step, the two symbols with smallest probabilities are selected, added to the top of the partial tree, deleted from the list, and replaced with an auxiliary symbol representing both of them. When the list is reduced to just one auxiliary symbol (representing the entire alphabet), the tree is complete. The tree is then traversed to determine the codes of the symbols. This is best illustrated by an example. Given five symbols with probabilities as shown in Fig. 2. Symbol a4 is combined with a5 and both are replaced by the combined symbol a45 , whose probability is 0.2. There are now four symbols left a1 , with probability 0.4, and a2 , a3 and a45 with probabilities 0.2 each. We arbitrarily select a3 and a45 combine them and replace them with the auxiliary symbol a345 , whose probability is 0.4. Three symbols are now left, a1 , a2 , and a345 , with probabilities 0.4, 0.2, and 0.4, respectively. We arbitrarily select a2 and a345 , combine them and replace them with the auxiliary symbol a2345 , whose probability is 0.6.

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Fig. 2 Huffman encoding

Finally, we combine the two remaining symbols, a1 and a2345 , and replace them with a12345 with probability 1.The tree is now complete. It is shown in Fig. 3 with the root on the right and the five leaves on the left. To assign the codes, we arbitrarily assign a bit of 1 to the top edge, and a bit of 0 to the bottom edge, of every pair of edges. A character’s code is found by starting at the root and following the branches that lead to that character. The code itself is the bit value of each branch on the path, taken in sequence. This results in the codes 0, 10, 111, 1101, and 1100. The assignments of bits to the edges is arbitrary. The average size of this code is 0.4 × 1 + 0.2 × 2 + 0.2 × 3 + 0.1 × 4 + 0.1 × 4 = 2.2 bits/symbol, but even more importantly, the Huffman code is not unique. Some of the steps above were chosen arbitrarily, since there were more than two symbols with smallest probabilities. The same five symbols can be combined differently to obtain a different Huffman code (11, 01, 00, 101, and 100). Notice that the Huffman method cannot be applied to a two-symbol alphabet. In such alphabet, one symbol will be coded with 0 and the other will be coded with 1. The Huffman method cannot assign to any symbol a code shorter than one bit, so it cannot improve on this simple code. Also, symbols with equal probabilities don’t compress under the Huffman method. Since strings of such symbols normally make random text, and random text does not compress. There may be special cases where strings of symbols with equal probabilities are not random Fig. 3 Adaptive arithmetic coding

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and can be compressed but not with Huffman codes. Accordingly, Huffman Encoding was not recommended for our application as we care about both compression efficiency as well as execution speed and performance aspects. B. Shannon-Fano coding: lossless It is based on the probability of occurrence of a certain character and assigning a prefix code. First, the code develops a list of probabilities corresponding to the relative frequency of the characters. Second, it does some kind of sorting to this list, and then it divides the list into two parts such that the sum of the total frequency on the left is much close to it on the right. Then, it assigns zero to the left and one to the right, and finally it recursively applies the preceding steps till each symbol has its own corresponding code. C. Arithmetic coding: lossless It is based on Shannon’s definition of source entropy, which is a measure of source’s information and the average number of bits required to represent it. The message is encoded as a real number from 1 to 0. The main idea is representing each sequence of data, using a unique number. Basically, this is done by dividing a scale from 0 to 1 to number to intervals equal to the data symbols number, each interval proportional to the frequency to the corresponding symbol. Then each of these intervals is sub-divided, and the process go on till we get a unique interval for the required sequence. An example is shown in Fig. 3. We can notice that adaptive Arithmetic coding is very time costly algorithm, the adaptive probability modification is time consuming, and the algorithm is mainly sequential. Even with the parallelized implementation, it is still slower than other algorithms implementations. Therefore, it was a performance bottle neck for some applications [5].

D. Golomb-Rice (GR): lossless In Golomb-Rice (CR) encoding, given a divisor d, each input integer n is encoded into two parts: a quotient q in unary, and a remainder r in binary. They are calculated by the following equations: q=

n d

r = n mod d

(1) (2)

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RG coding is a subset of Golomb coding, restricting divisors to powers of two. This implies that for a given d, the number of bits required to encode the remainder portion is fixed to k = log2 (d) bits (otherwise variable with Golomb coding). This simple assumption/restriction has a practical negligible negative impact on compression ratio, but greatly simplifies the encoding/decoding process by allowing the use of simple shift operations instead of the more complex division. Good choices of d (and hence k) greatly affect the compression ratio, d is a hyper parameter that is chosen depending on data distribution. For example, if d = 4 (i.e. k = 2) and n = (17)10 = (0001 0001)2 , from Eq. (1) q will be (0001 00)2 = (4)10 = (1111)unary in unary and from Eq. (2) r will be (01)2 = (4)10 . Therefore, the output encoded binary data is concatenating r and q along with 0 delimiter bit, in our example: (01 1111 0)2 . RG encoding is used for lossless compression of data that has a Gaussian distribution otherwise it will lead to huge data expansion. We may conclude that it may be used to encode errors from predictors as error has a Gaussian distribution by nature. We may also notice that RG encoded data has long runs of ones and high repetitions of some data sequences. Therefore, we may conclude that following RG encoding by another compression technique may lead to promising results. It should also be noted that RG encoding is a simple, fast and parallelized technique [6]. Therefore, it’s a low latency encoding algorithm. E. ORing-bits: lossless This method starts with a sparse string L1 of size n1 bits. In the first step, L1 is divided into k substrings of equal size. In each substring all bits are logically OR-ed, and the results (one bit per substring) become string L2 , which will be compressed in step 2. All zero substrings of L1 are now deleted. Here is an example of a sparse, 64-bit string L1 , which we divide into 16 substrings of size 4 each L1 = 0000|0000|0000|0100|0000|0000|0000|1000|0000|0000|0000|0000|0010|0000|0000|0000. After ORing each 4-bit substring we get the 16-bit string L2 = 0001|0001|0000|1000. In step 2, the same process is applied to L2 , and the result is the 4-bit string L3 = 1101, which is short enough, so no more compression steps are needed. After deleting all zero substrings in L1 and L2 , we end up with the three short strings L1 = 0100|1000|0010, L2 = 0001|0001|1000, L3 = 1101. The output stream consists of seven 4-bit substrings instead of the original 16 but a few more numbers are needed, to indicate how long each substring is. Therefore, we conclude that ORing bits algorithms give an efficient compression when data is sparse where zeros are dominant. It also may be used to compress flipped version of binary data where ones are dominant [41–43].

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Wireless Communication Systems: Compression …

Image Compression

Sometimes we need to compress some images that have redundancy in their bits. There are three types of redundancy: the first is Psycho-visual redundancy, in which human eye perception decides on if some pixels are important or can be ignored, the second is interpixel redundancy, where there are statistical dependencies among pixels, and the last one is called coding redundancy, in which variable length code, like Huffman and arithmetic coding, is used instead of full length one. As stated earlier, there are two types of data compression: Lossy and Lossless. Lossy image compression reaches a high compression ratio, 50:1 or higher, because it allows some acceptable degradation. On the other hand, lossless compression completely recovers the original data so this reduces the compression ratio to 2:1. Generally, lossy compression consists of three main steps: transform coding, quantization and entropy encoding. Each one of them has its own redundancy type. Transform coding is to eliminate the inter-pixel redundancy to pack information efficiently. Quantization deals with psych-visual redundancy to represent the packed information with very few bits. Then, the quantized bits are encoded to get compressed. Quantization means replacement of a set of values with only one value. There are two types of quantization: scalar quantization and vector quantization. The difference is that the latter replaces each block of input pixels with the index of a vector in the code block. The decoder receives each index and maps it with the corresponding vector in the code block. Unlike lossy compression, lossless one is a two-step algorithm: the first one turns the original image format into another format where inter-pixel redundancy is reduced, and the second one uses an entropy encoder to totally remove the coding redundancy. There are also many methods for lossless compression but the most popular and used ones are Run Length encoding. A. Run Length encoding: lossy There is a pair of value and length; value is the repeated value and length is the number of repetitions, predictive coding where the value of each pixel is predicted by using the value of the neighboring pixels such that each pixel is predicted with a certain prediction error, Entropy coding that is about the minimum size necessary to convey a particular amount of information, and multiresolution coding where It starts with a low-resolution version of the original image, and interpolates the pixel values to successively generate higher resolutions. The basic run length encoding (RLE) technique is based on the idea of counting repetitions. RLE is a very simple form of lossless data compression in which runs of data, that are sequences in which the same data value occurs in many consecutive data elements, are stored as a single data value and count, rather than as the original run. For example, if input data is {AAAAAAABBBCCCAAAAAAAA}, the encoded output will be {7A3B3C8A}. In the previous example, compressed data is formed of a number that represents how many times the following byte was repeated in the input

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sequence. It should also be noted that the RLE is a sequential algorithm that processes data sequentially in order to count how long a run is. Some implementations make use of the nature of the binary data to process a block in parallel instead of processing it sequentially. These implementations use a combinational circuit to determine the rising and falling edges of a certain run [7]. B. Lempel–Ziv (LZ): lossless There are many variations of Lempel–Ziv encoding. Here we present two variations of it. • LZ77 The main idea of this method (which is sometimes referred to as LZ1) is to use part of the previously seen input stream as the dictionary. The encoder maintains a window to the input stream and shifts the input in that window from right to left as strings of symbols are being encoded. The method is thus based on a sliding window. The window below is divided into two parts. The part on the left is called the search buffer. This is the current dictionary, and it always includes symbols that have recently been input and encoded. The part on the right is the look-ahead buffer, containing text yet to be encoded. In practical implementations the search buffer is some thousands of bytes long, while the look-ahead buffer is only tens of bytes long [8]. Code word is formed by concatenation of (pointer, length, last symbol), where the pointer is the index of longest match of the string starting with the first position of the look ahead buffer is found in the search buffer. This can be shown in Fig. 4. • PDLZW Parallel Dictionary Lempel–Ziv-welch, an example to illustrate the operation of PDLZW compression algorithm is shown in Fig. 5. Here assume that the alphabet set is {a; b; c; d} and the input string is “aaabbccbbccaaa”. The dictionary address space is Fig. 4 LZ77 working technique illustration

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16. The initial dictionary contains all single characters: a; b; c; and d. Figure 5 illustrates the operation of PDLZW compression algorithm [9]. In the conventional dictionary implementation of LZW algorithm, it uses a unique dictionary with large address space so that the search time of the dictionary is quite long. In this design the unique dictionary is replaced with a dictionary set that is composed of several smaller dictionaries of different address spaces and word widths. The variableword-width dictionary used in PDLZW compression or decompression algorithms is partitioned into m smaller variable-word-width dictionaries, numbered from 0 to m-1, with each of which increases its word width by one byte. That is to say, dictionary 0 has one-byte word width, dictionary 1 two bytes, and so on. For decompressing the original substrings from the input compressed codewords, each input compressed codeword is used to read out the original substring from the dictionary set. To do this without losing any information, it is necessary to keep that the dictionary

Fig. 5 An example to illustrate the operation of PDLZW compression algorithm

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sets used in both algorithms have the same contents. The update operation of the dictionary set is carried out by adding the concatenated substring of the last output substring and the first character of the current output substring as a new entry. From the previous description, we conclude that both algorithms may achieve high compression ratios, but they require much larger block size than the size used in our application to preform effectively, and due to their sequential and dictionary-based nature, it will result in a high latency which contradicts the primary constraint of our application. C. 842B Technique: lossless For now, we have mentioned the basic compression algorithms. The main disadvantage of them all is that they are highly sequential, based on a statistical and dictionary-based techniques. We have mentioned before that in some applications the compression speed is as important as the compression results that the compression is needed to be performed with low latency. Some solutions were developed for that purpose. 842B is an algorithm make use of the parallelization and pipelining luxuries that hardware provides [10]. It identifies the repeating patterns of size 8, 4 and 2 bytes in the input data stream and replaces them with 6–8-bit pointers to previously seen data. Each 8-byte chunk of the input data is divided into 7 phrases, as shown in Fig. 6, which are compared against previously seen phrases. Dictionaries are used to store the input phrases for lookup in processing subsequent inputs. For constant-time phrase look-up, the address of the phrase in the dictionary (pointer) is stored in a hash table, at a location given by the hash value of the phrase. During compression, the 7 sub-phrases of the 8B input are hashed into 7 keys, which are used to read the pointers from the hash tables. Using these pointers, 7 phrases are read from the dictionaries and compared against the input sub-phrases. The compressed output is generated as the smallest possible combination of the pointers to the matching phrases in the dictionaries and the unmatched (raw) phrases. A 5-bit template is prefixed to the encoded data, indicating the composition of the compressed data. Fig. 6 8-bytes input splits into 7 phrases

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Decompression involves decoding the template and extracting different pointers and raw phrases from the compressed data, reading the remaining phrases from the dictionaries and reconstructing the uncompressed data. Reading the phrases from the dictionaries requires reconstructing the dictionary contents. The dictionary is reconstructed on the fly, much as in the compression operation, by simply writing the post-decompression phrases back into the dictionaries. Note that no hashing and no hash tables are required during decompression since the pointers are already present in the compressed data. This algorithm is hardware friendly one. It is parallelized as it processes 8 bytes input chunk in parallel, pipelined where the result of compressing 8 bytes input is available every clock cycle. Compressing a large frame composed of 64 bytes for example need to be performed sequentially (processing 8 bytes at a time) in order to allow the algorithm to construct the dictionary and get a remarkable compression ratio. Another limitation is that using large dictionaries need larger addresses, so dictionary size is a design parameter that strongly affects the compression efficiency. D. Predictive Encoding Technique: lossy It is an approach that is commonly used in image compression [11]. It is based on inter pixel redundancies elimination of closely spaced pixels. That can be achieved by extracting and coding only the new information in each pixel. The new information may be considered as the difference between the actual and the predicted value of that pixel. This prediction method is dependent on the type of the predictor used. The predictive encoding can be used as a lossy compression technique and can be used as a lossless compression technique. The lossless approach is based upon the idea of the prediction of the pixel value, then calculating the error between the predicted value and the actual value, then this error values are encoded using a sequence encoder which produces the compressed sequence. The decompression is performed by decoding the compressed sequence, then by knowing the error and the predicted parts of the image the original image can be restored. This approach is discussed as a block diagram in Fig. 7 [4]. The sequence encoder should be an encoder that compresses data that have the normal or Gaussian distribution as the error values are often having that distribution. It should also be mentioned that a high quality predictor will produce an error distribution that follows the Gaussian distribution as the error values of a good predictor should be of a high density near the zero error value and less dense as we go far away from the zero error value.

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39

e(n)

+

x(n)

-

u(n) Predictor

Fig. 7 Predictive encoding block diagram

E. Neural Networks Compression: lossy Artificial neural networks are computing systems that are inspired by, but not necessarily identical to, the biological neural networks that constitute animal brains. Such systems "learn" to perform tasks by considering examples, generally without being programmed with any task-specific rules. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called ’edges’. Artificial neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer (the input layer), to the last layer (the output layer), possibly after traversing the layers multiple times. An autoencoder is a type of artificial neural network used to learn efficient data coding in an unsupervised manner. The aim of an autoencoder is to learn a representation (encoding) for a set of data, typically for dimensionality reduction, by training the network to ignore signal “noise”. Along with the reduction side, a reconstructing side is learnt, where the autoencoder tries to generate from the reduced encoding a representation as close as possible to its original input, hence its name.

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Autoencoder is used in data compression as it is a sort of dimensionality reduction. This can be illustrated in Fig. 8 where the input layer of the autoencoder is the raw data to be compressed. The raw data goes through the encoder layers which are the layers before the layer that represents the compressed data. For data reconstruction, the compressed data goes through the decoder layers which are the layers that follows the compressed data in Fig. 8. Since the efficiency in an artificial neural network is not 100%, therefore the reconstructed data are not ensured to be the same as the input data. That is why an autoencoder is a sort of a lossy compression [12]. Lossless compression can be constructed by encoding the error in the reconstructed data in a manner similar to what was mentioned in predictive encoding technique where a compression round includes encoding input data followed by decoding the input data, then the reconstruction error is calculated and encoded [13–15].

Input Data

Reconstructed data

Compressed data

Fig. 8 Auto encoder neural network diagram

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Compression Techniques

41

F. GZIP: lossy Until now, we have mentioned standalone compression techniques only. Some techniques prefer to use hybrid solutions where the solution combines 2 or more basic compression technique. GZIP uses a variation of LZ77 encoding, static Huffman encoding and dynamic Huffman encoding. GZIP is based on the DEFLATE algorithm, which is a combination of LZ77 and Huffman coding. DEFLATE was intended as a replacement for LZW and other patent-encumbered data compression algorithms which, at the time, limited the usability of compress. G. Transformation-based Compression: lossy Transform coding is considered a general scheme for lossy image compression, where it uses a reversible and linear transform to turn the original image into a set of coefficients in another domain to be quantized and coded later. There are various transforms, each of which is used in a specific application. For instance, Discrete Karhunen–Loeve transform (KLT) depends on Hotelling transform and used for information packing but difficult to compute, Discrete Fourier Transform (DFT) and Discrete cosine Transform (DCT) approximate the energy-packing efficiency of the KLT, and have more efficient implementation. • DCT Compression DCT is a proven to be efficient for image compression. The major advantage in DCT is that its kernel remains fixed irrespective of the input image, thus computational cost is reduced. JPEG and MPEG compression standards also use this transform. It is a block transform which is applied over non-overlapping blocks in an image. In general, DCT block sizes are having size 8 × 8 or 16 × 16. • Wavelet-Based Compression The discrete wavelet transforms is a powerful technique in image processing, in which the wavelet converts the image into a series of wavelets that are stored more efficiently compared to blocks of pixels. For one dimension, the signals are divided into two parts: high and low frequencies. The Wavelet compression offers an approach that allows one to reduce the size of the data while at the same time improving its quality through the removal of high-frequency noise components. Wavelets are functions which allow data analysis of signals or images, according to scales or resolutions. The processing of signals by wavelet algorithms in fact works much the same way the human eye does; or the way a digital camera processes visual scales of resolutions, and intermediate details. An inverse wavelet transform is performed to decompress the image [1]. The difference between the

42

Wireless Communication Systems: Compression …

Fourier transform (FT) and wavelet transform (WT) is that FT breaks down a signal into constituent sinusoids of different frequencies and WT breaks down a signal into shifted and scaled version of the original/mother wavelet [4]. • KLT Compression It is called optimal transform as it reconstructs the image from minimum number of coefficients present in transform domain. KLT also gives the least mean square error between the original image and reconstructed image. The major drawback of this transform is its computational cost as, the kernel needs to be calculated each time a new image is considered for compression. During reconstruction the performance of receiver is affected as it needs more of side information every image.

2.3

Video Compression

Videos is now one of the most used form for displaying data as technology proceeds the quality of the videos increases in order to show the smallest details. Nowadays the resolution increased to 4 K. This indicates that the size of the recorded videos increased. Many surveillance applications require to send the video from one location to another or to store them in data centers for further use. In order to make it feasible to send or store high quality videos we need to decrease their size. Many algorithms are used to decrease the size of the videos with maintaining high quality. Some of them may lose some data which doesn’t affect the quality much (lossy) while others compress all the data (lossless). Videos mainly composed of many frames of still images therefore the compression of videos may have similarities with compressing images. In order to decreases the amount of sent bits similar frames are decoded together to avoid sending the same data several times. This happens in frames with slow movements where several bits are repeated many times. Other approach is by using motion prediction techniques which can predict the next frame. The first frame to be encoded is called the initial frame which every compression process should start with. The predicted frames and the similar frames are grouped with their initial frame. Grouping frames doesn’t mean that they have to be decoded in the same viewing order, however some later frames may be decoded before other in order to decrease the amount of sent data. A. Joint Photographic Experts Group (JPEG) This compression algorithm depends on elimination of the redundant data. High compression ratios may be achieved using this algorithm up to 100:1. However, the average compression ratio used is 16:1 in order to save the quality of the video. This algorithm deals with the frame as three 2-D arrays where one is responsible for the luminesce

2

Compression Techniques

43

and the other two arrays are responsible for the coloring. The pixies of the array are divided into subarrays of 8 × 8 arrays. Discrete cosine deformation (DCT) as well as fast Fourier transform (FFT) are applied to transform the arrays into the frequency domain. Where zero and near zero frequencies are eliminated to zeros to decrease the number of bits sent. Also, frequencies are rounded to the nearest frequency to shorten the amounts of bits. JPEG algorithm is a symmetric compression method. This means that the complexity for compression is similar to that of the decompression. In the decoding process inverse DCT process is done to return to the original pixel domain. Although the compression time of this algorithm is fast, at typical compression ratios it is not considered to be very efficient time wise. The algorithm is based in two visual effects of the human visual system. First, humans are more sensitive to the luminance than to the chrominance. Second, humans are more sensitive to changes in homogeneous areas, than in areas where there is more variation (higher frequencies). Conversion of a digital image into JPEG ‘compressed’ image takes place in a series of steps as shown in Fig. 9: 1. Divide your image into 8 × 8 blocks. 2. If it is RGB convert it into Y’CBCR -grey scaling, blueness and redness of an image-, -brightness and colour-. 3. Use DCT to convert the source image from spatial domain into frequency domain.

Fig. 9 JPEG compression- decompression block diagram

44

Wireless Communication Systems: Compression …

4. Apply quantization using the suitable quantization table according to the required quality needed. 5- Encode your image using Huffman table, to have your jpeg ready with the needed quality. B. Moving Picture Experts Group (MPEG) MPEG algorithm is based on encoding key frames and predict the motion from these frames this allows the algorithm to reach high compression ratio of 100:1 the encoder rearrange the images to make the predicted images come after the reference one. The decoder on the other side put the images in the right order. The difference between the encoding and the decoding processes makes the algorithm asymmetric in complexity. There are many versions of MPEG. MPEG-1 is used in recording data on CDs as it allow decoding in fast speed which is close to the speed of the rotation of the CD inside the CD ROM. MPEG-2 is used in television broadcast it has more than audio channel to surround audio in different direction. C. H.265(HEVC) H.265 is also called High efficiency video coding it is design to increase the data compression ratio where redundant pixels are replaced by short description from previously encoded pixels and frames. One of the new features of HEVC is that it increases the size of the encoded array from 8 × 8 to 16 × 16 and even to 64 × 64 [16]. This improves prediction within the same frame as well as the next frames. If the coded video is to be broadcasted live over a network, the encoding process is time limited and required to quickly produce a highly compressed video with good enough quality. HEVC Encoding- Decoding process takes place in a series of steps as shown in Fig. 10: • Partitioning: as shown in Fig. 11, divide each frame into tiles or slices till you reach one macro-block (16 × 16 pixels) or CTU (Coding Tree Unit) which can be up to 64 × 64 pixels in size. Video codec works on one CTU at a time. The CTU will be stored and processed as 3 components: • Prediction: it predicts the current frame from data we already have or the previous frame, by predicting the motion vector of the changing part of the frame which is not enough so it’s followed by subtracting the predicted frame from the current frame will give us the difference between the two frames the so-called residual, i.e. The residual will have little information if the predicted frame is close to the actual current frame and vice versa. The prediction can be performed within a picture (intra) which only happens within each frame or between adjacent pictures (inter) in the video. Sequence which involves the analysis of the changes in the movie from frame to frame and makes note of only the parts of the image that have changed. The initial frame that

2

Compression Techniques

45

Fig. 10 HEVC encoding- decoding block diagram

Fig. 11 Partitioning phase

is used to compare the others to is called a key-frame! Obviously when there is a dramatic change from one frame to the next, as happens when one shot in a movie cuts to another, a new key-frame must be saved by the compressor. As it plays, a movie compressed with inter-frame compression only redraws the part of the frame that has changed. Instead of saving a complete image for each frame of a movie, it only saves a portion of the image. More key-frames means a smoother movie, but also means a bigger file. • Transform: transforms the residual into frequency domain using DCT and quantizes it to be easily stored. • Entropy encoding: encoding the bits of information to be easily stored and transmitted. Each symbol is coded by considering the prior data. Encoded data must be read

46

Wireless Communication Systems: Compression …

from the beginning, there is no random access possible. Each real number (< 1) is represented as binary fraction.

2.4

Audio Compression

Audio compression is the process of reducing the transmission bandwidth and the storing requirement of audio data. This done by removing the redundant, implicit in the remaining information, or otherwise any irrelevant, insignificant, information from the audio signal. Audio compression algorithms are often referred to as audio encoders and are implemented in software as audio codecs [5, 6, 8–10, 17–19]. There are two Audio compression types: • Lossless audio compression: Lossless formats are preferable for people who wants to backup original audio CDs. they remove redundant data and use compression algorithms that preserve audio data. As a result, the resultant compressed audio file is exactly the same as the original source when it is decompressed [17]. • Lossy audio compression: Lossy audio compression algorithms provide higher compression ratio than the lossless compression algorithms. They remove the irrelevant information from the audio file. As a result, the resultant compressed audio file is similar but smaller than the original source when it is decompressed [17]. A. Lossless Audio Methods

• Lossless Predictive Audio Compression (LPAC): it is an algorithm developed by a German engineer called Tilman Liebchen. Long time ago, it was the popular format as it offered versions available for Linux and Solaris. But it was not an open source format. In addition, LPAC lost its popularity to Free Lossless Audio Codec (FLAC) when the multiplatform ability was compared [18]. • Free Lossless Audio Codec (FLAC): it is a lossless open source format. It supports album art, audio tags, and is suitable for listening, archiving and recording. FLAC is similar to the MP3, a lossy method that will be illustrated later, audio format. It supports hardware and can be ported to many platforms and systems. It offers a good streaming, and the decoding is fast, independent of the compression ratio. Unfortunately, when compared with the other recent formats FLAC has less efficient compression ratios [19]. • Apple Lossless Audio Codec (ALAC): it is the compression format used for the songs and albums bought from iTunes store. The file extension used for it is m4a which is the same as for the default AAC, a lossy method that will be illustrated later, format.

2

Compression Techniques

47

It is preferred because; it offers a safely conversion to other formats without degrading the quality. Unfortunately, it has many disadvantages such as: large size and it is less compatible with the hardware Compared to popular lossy formats [6]. • True Audio (TTA): it is real-time audio compression format. It offers suitable compression levels while in addition to high operation speeds. It gained its popularity as it works with the minimal system requirements. However, it lost its popularity again as the TTA files are not supported by major part of audio players and have to be converted to other formats in order to be played on the audio players [8]. • Windows Media Audio (WMA): it is a lossless audio codec, developed by Microsoft that competes with most of the compression formats. It is an available format for nearly any operating system used today [9]. B. Lossy Audio Compression

• Advanced Audio Coding (AAC): it is a lossy compression format which was standardized by the MPEG group. It generally achieves better sound quality when compared with MP3 at similar bit rates. AAC is the standard audio format for Apple’s iPhone, iPod, iPad, iTunes, Sony’s PlayStation 3 and YouTube. Also, it is supported by Nokia/Windows Phone, Android, Sony Walkman, Sony’s PlayStation Portable, Nintendo’s Wii, HDTV, Apple TV, DVB and more [11]. It is offers a good quality in a small size in addition to the Short decode times. But, unfortunately it takes long time to encode besides the high utilization of CPU when it encodes or decodes [12]. • Adaptive differential pulse-code modulation (ADPCM): it is a method used to convert analog signals to binary signals. The technique takes frequent samples of the sound and represent the value of the sampled data in a binary form. The ADPCM technique is especially used to send sound signals through fiber-optic over long-distance lines. Which is useful for organizations that set up digital lines between remote sites to broadcast both voice and data. As it is used to digitalize the voice signals before broadcasting it. In the telecommunication field, the ADPCM technique is used mainly in speech compression due to the ability of reducing bit flow without compromising quality [20]. • Dolby Digital (AC-3): it is perceptual digital audio coding technique that compress the audio by reducing the amount of data needed to produce high-quality sound. It works on the fact that the human ear screens out a certain amount of sound that is considered a noise to the ear. As a result, this technique reduces the amount of data by eliminating this noise [21]. • MPEG-1 or MPEG-2 Audio Layer III (MP3): it is a well-known and widely used audio coding format for digital audio. MP3 compression commonly achieves 75– 95% reduction in size. This means MP3 files are thus 1/4–1/20 the size of the original

48

Wireless Communication Systems: Compression …

digital audio stream. It was designed by the MPEG group as a part of its MPEG-1 standard and later extended in the MPEG-2 standard. Like AC-3, MP3 takes the advantage of the perceptual limitation of human hearing to reduce the amount of data needed [22]. MP3 compression works by reducing (or approximating) the accuracy of certain components of sound that are considered (by psychoacoustic analysis) to be beyond the hearing capabilities of most humans. This method is commonly referred to as perceptual coding or as psychoacoustic modelling. The remaining audio information is then recorded in a space-efficient manner, using MDCT and FFT algorithms. Compared to CD-quality digital audio, MP3 compression can commonly achieve a 75–95% reduction in size. For example, an MP3 encoded at a constant bitrate of 128 kbit/s would result in a file approximately 9% of the size of the original CD audio. The Algorithm: as seen in the following diagram, the process of MP3 compression can be broken down into steps. First, the input audio stream passes through a filter bank that divides the sound into sub-bands of frequency. Simultaneously, it passes through a psychoacoustic model that utilizes the concept of auditory masking to determine what can or cannot be heard in each sub-band. The bit allocation block minimizes the audibility of noise. Finally, the bit stream formatting block accumulates all the information and processes it into a coded bit stream. • Dolby Digital (AC-3): it is perceptual digital audio coding technique that compress the audio by reducing the amount of data needed to produce high-quality sound. It works on the fact that the human ear screens out a certain amount of sound that is considered a noise to the ear. As a result, this technique reduces the amount of data by eliminating this noise [21]. • MPEG-1 or MPEG-2 Audio Layer III (MP3): it is a well-known and widely used audio coding format for digital audio. MP3 compression commonly achieves 75– 95% reduction in size. Which means MP3 files are thus 1/4–1/20 the size of the original digital audio stream. It was designed by the MPEG group as a part of its MPEG-1 standard and later extended in the MPEG-2 standard. Like AC-3, MP3 takes the advantage of the perceptual limitation of human hearing to reduce the amount of data needed [22].

4

Comparative Study for All Different …

3

Performance Metrics

3.1

Compression Ratio

49

The ratio between the original file and the compressed file is very important in deciding which algorithm will be used. As the size of the compressed file decreases the compression ratio increases. But this is not the only parameter to consider as in order achieving high compression ratios the complexity and time increases. The best solution is to find a compromise between all these parameters [23]. Compression ratio (CR) is obviously the main compression metric. It measures the functionality of compression. It can be defined as the ratio between data size before compression to data size after compression. For a successful compression, the compression ratio is higher than one. Compression ratio can be calculated according to the following equation: CR =

3.2

I nput data si ze Out put data si ze

Processing/Compression Time and Speed

The compression process is a time consuming process that sometimes requires special hardware. Compression time is a critical parameter in choosing an algorithm. Some applications need the compression process done in real-time. Like communication and TVbroadcasting. Other application doesn’t require high speed algorithms like music movies releases. There is always a tradeoff between compression efficiency and compression speed in these applications [24].

4

Comparative Study for All Different Compression Techniques

A comparison is held between different algorithms where each algorithm is investigated from others previous work [7, 25–40]. The comparison criteria are the compression ratio (CR) which is the compression ratios achieved in the referred work that uses a specific technique. It should also be noted that the compression ratio achieved by a certain algorithm or technique is not a property of that technique as the compression ratio depends on the data distribution and data size on which that certain technique is applied on. For that reason, the conditions, in which each technique is used in, was mentioned in the “when to use” column. The conditions in which each algorithm leads to a great failure is also mentioned in the “when not to use” column. It is also mentioned if the technique is dictionary based or not. The comparison results are shown in Tables 1 and 2.

50

Wireless Communication Systems: Compression …

Table 1 Comparison between different algorithms: usage scenarios Algorithm name

CR

Dictionary based

When to use

When not to use

Can be parallelized

842B [10]

2

Yes

High speed

Dictionary size limitations

No

Golomb-Rice

–

No

Gaussian distribution

Otherwise

Yes

Arithmetic

2

No

High entropy

High throughput

No

Run-length [20] 3.6

No

Data has many runs

No sequences

No

Dynamic Huffman

–

Yes

Different data frequencies

equal probabilities

No

LZ1

–

Yes

Repetitive text data

small block size

No

DLZW [9]

1.8

Yes

Repetitive text data

Dictionary size limitations

No

Table 2 Different algorithms for compression: text, audio, image, video, and binary data Approach Lossless

Entropy

Algorithm Prefix Huffman Shanon-Fano Unary Arithmetic

Others

Predictive

Dictionary

Deflation

Text √

Audio √

Data binary √

Image √

Video √

√

√

√

√

√

√

√

√

√

√

√

√

√

√

√

√

√

√

√

√

–

–

–

Area image

–

–

–

DPCM

–

– √

–

FLAC

–

TTA

–

ALAC

–

√ √ √

√ √ √

– – –

–

–

–

–

–

–

–

–

–

–

– √

WMA

–

MED

–

–

–

GAP

–

–

–

GED

–

–

–

Run-length

–

–

–

Bit-pair

–

–

–

LZ

–

–

–

√ √ √ √ √

– – – – √ – – (continued)

References

51

Table 2 (continued) Approach Lossy

Transform (frequency)

Other

5

Algorithm

Text

Audio

Data binary

LZW

–

–

–

Karhunen–Loeve

–

–

–

DCT

–

–

–

DWT

–

–

–

Image √ √ √ √

H.264/AVC

–

–

–

–

H.261

–

–

–

– – √

H.263

–

–

–

Fractal

–

–

–

Predictive

–

–

–

Chroma subsampling

–

–

–

√ √

Video – – √ – √ √ √ – – –

Conclusions

Data compression is an important part of information security because compressed data is more secure and easy to handle. Effective data compression technology creates efficient, secure, and easy-to-connect data. There are two types of compression algorithm techniques, lossy and lossless. These technologies can be used in any data format such as text, audio, video, or image. Data compression is typically applied to reduce the amount of data to be downloaded, hence improving effective transmission capacity. In this Chapter, a comprehensive study of different compression techniques for wireless systems is presented.

References 1. M. Song, Color Image Compression with Wavelets, Preprint (2004) 2. A.M. Tekalp, Digital Video Processing (Prentice Hall Press, 2015) 3. Y. Ming, N. Bourbakis, An overview of lossless digital image compression techniques in Proceedings of the 48th Midwest Symposium on Circuits and Systems, Covington, KY, USA, vol. 2 (7–10 Aug 2005), pp. 1099–1102 4. C.S. Lai, Compression of power system signals with wavelets, in International Conference on Wavelet Analysis & Pattern Recognition IEEE (2014) 5. What is MPEG video format, Winxdvd.com (2017). [Online]. Available: https://www.winxdvd. com/resource/mpeg.htm 6. What is the ALAC codec option in iTunes? Lifewire (2017). [Online]. Available: https://www. lifewire.com/what-is-alac-audio-format-2438531. Accessed 03 Oct 2017 7. J. Trein, B. Hoppe, Hardware implementation of a run length encoding compression algorithm with parallel inputs, in ISSC Galway, 8–19 June 2008

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8. What is TTA? TTA audio file format description, Coolutils.com (2017). [Online]. Available: https://www.coolutils.com/Formats/TTA 9. Windows media audio, En.wikipedia.org (2017). [Online]. Available: https://en.wikipedia.org/ wiki/Windows_Media_Audio#Windows_Media_Audio_Lossless 10. Data compression, En.wikipedia.org (2017). [Online]. Available: https://en.wikipedia.org/wiki/ Data_compression#Audio 11. What is AAC -advanced audio coding—format, Winxdvd.com (2017). [Online]. Available: https://www.winxdvd.com/resource/aac.htm 12. Advanced audio coding (AAC) pros and cons, Tom’s Tek Stop (2017). [Online]. Available: https://tomstek.us/advanced-audio-coding-aac-pros-and-cons/ 13. S. Zebang, K. Sei-ichiro (2019). Densely connected AutoEncoders for image compression, in Proceedings of the 2nd International Conference on Image and Graphics Processing—ICIGP’19. https://doi.org/10.1145/3313950.3313965 14. Z. Cheng, H. Sun, M. Takeuchi, J. Katto, Deep convolutional autoencoder-based lossy image compression. https://arxiv.org/abs/1804.09535 15. Image Compression Using Autoencoders in Keras 16. G. Sullivan, J. Ohm, W. Han, T. Wiegand, Overview of the high efficiency video coding (HEVC) standard. IEEE Trans. Circuits Syst. Video Technol. 22(12), 1649–1668 (2012) 17. SMAK, Lecture 8 Audio Compression, Slideshare.net (2017). [Online]. Available: https://www. slideshare.net/MrSMAk/lecture-8-audio-compression 18. Lossless predictive audio compression—hydrogenaudio knowledgebase, Wiki.hydrogenaud.io (2017). [Online]. Available: http://wiki.hydrogenaud.io/index.php?title=Lossless_Predictive_A udio_Compression 19. What is free lossless audio codec (FLAC)?—Definition from Techopedia, Techopedia.com (20170. [Online]. Available: https://www.techopedia.com/definition/3271/free-lossless-audiocodec-flac 20. What is adaptive differential pulse code modulation (ADPCM)?—Definition from Techopedia, Techopedia.com (2017). [Online]. Available: https://www.techopedia.com/definition/5877/ adaptive-differential-pulse-code-modulation-adpcm 21. What is AC-3 (Dolby Digital)?—definition from WhatIs.com, WhatIs.com (2017). [Online]. Available: http://whatis.techtarget.com/definition/AC-3-Dolby-Digital 22. MP3, En.wikipedia.org (2017). [Online]. Available: https://en.wikipedia.org/wiki/MP3 23. N. Sharma, U. Batra, Performance analysis of compression algorithms for information security: a review. ICST Trans. Scalable Inf. Syst. 7(27) (2018). Art. no. 163503 24. Z. Ning, Z. Jinfu, ‘Study on image compression and fusion based on the wavelet transform technology. Int. J. Smart Sens. Intell. Syst. 8(1), 480–496 (2015) 25. D.J. Craft, A fast hardware data compression algorithm and some algorithmic extensions. IBM J. Res. Dev. 42(6) (1998) 26. B. Abali, H. Franke, D.E. Poff, R.A. Saccone, C.O. Schulz, L.M. Herger, T.B. Smith, IBM memory expansion technology (MXT). IBM J. Res. Dev. 45(2), 287–307 (2001) 27. B. Sukhwani, B. Abali, B. Brezzo, S. Asaad, High-Throughput, lossless data compression on FPGAs, in IEEE International Symposium on Field-Programmable Custom Computing Machines (2011) 28. J.L. Núñe, S. Jones, Gbit/s lossless data compression hardware, in IEEE Transaction on Very Large Scale Integration (VLSI) Systems (2003) 29. J. Fowers, J.-Y., D. Burger, A scalable high-bandwidth architecture for lossless compression on FPGAs, in 2015 IEEE 23rd Annual International Symposium on Field-Programmable Custom Computing Machines (2015)

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Wireless Communication Systems: Confidentiality Encryption and Decryption

In common communication systems confidentiality are ensured exclusively by encryption of the messages. This chapter covers the security procedures in wireless communication systems. The goal of physical layer security is to make use of the properties of the physical layer to enable critical security aspects. Moreover, wireless communication services are rapidly increasing because of the proliferation of mobile devices and the advent of the Internet of Things (IoT). Authentication is an important issue in wireless communications because the open nature of the wireless medium provides more security vulnerabilities. Most of existing wireless communication systems achieve the authentication goal via upper-layer authentication mechanisms. The security of upper-layer authentication mechanisms is ensured by using conventional cryptography-based algorithms. Wireless security prevent unauthorized connections and attacks to the network such as Eavesdropping, Brute force attack, Statistical attack, Jamming (Denial of Service).

1

Symmetric Encryption

Symmetric means sender and receiver has a shared secret key. There are two primitive operations with which strong encryption algorithms can be built: confusion and diffusion. In confusion, an encryption operation where the relationship between key and ciphertext is obscured and a common element for achieving confusion is substitution. In diffusion, an encryption operation where the influence of one plaintext symbol is spread over many ciphertext symbols with the goal of hiding statistical properties of the plaintext and a common element for achieving diffusion is through permutations (i.e., transposition). Avalanche effect is considered as one of the desirable property of any encryption algorithm. A slight change in either the key or the plain-text should result in a significant change in the cipher-text [1–3]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 K. S. Mohamed, Wireless Communications Systems Architecture, Synthesis Lectures on Engineering, Science, and Technology, https://doi.org/10.1007/978-3-031-19297-5_3

55

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1.1

Stream Ciphers

Stream ciphers operate on pseudorandom bits generated from the key, and the plaintext is encrypted by XORing both the plaintext and the pseudorandom bits. Stream ciphers use conceptual tools similar to block ciphers. Substitution is the primary tool: each bit or byte of plaintext is combined with the key material by an exclusive-or (XOR) operation to substitute the plaintext bit into the ciphertext bit. Binary XOR is quite simple. There are only two possible values, 1 or 0, and if the two inputs are the same the result is 0, otherwise it is 1 [4]. Stream ciphers security depend entirely on the “suitable” key stream, while randomness plays a main role, so the random number generator (RNG) are significant for that purpose. Neural cryptography is a new source for public key cryptography schemes which are not based on number theory, and have less computation time and memory complexities. Neural cryptography can be used to generate a common secret key between two parties [5].

1.2

Block Ciphers

In a block cipher, two values are generally referred to: the size of the block and the size of the key. The security relies on the value of both. Many block ciphers use a 64-bit block or a 128-bit block. As it is crucial that the blocks are not too large, the memory footprint and the ciphertext length are small in size. Regarding the ciphertext length, blocks instead of bits are processed in a block cipher. Block ciphers can be symmetric or non-symmetric as will be discussed later in this chapter. We start with plaintext. Something you can read. We apply a mathematical algorithm to the plaintext. The algorithm is the cipher. The plaintext is turned in to ciphertext. In symmetric encryption, same key is used to encrypt and decrypt the data. The shared key K between sender and receiver should be kept secret. The encryption/decryption process can be modeled by Eq. (1). P = D K (E k (P))

(1)

Symmetric encryption is fast to encrypt and decrypt, suitable for large volumes of data.

1.2.1

DES

The Data Encryption Standard (DES) is a symmetric-key block cipher published by the National Institute of Standards and Technology (NIST). DES is an implementation of a Feistel Cipher. It uses 16 round Feistel structure. The block size is 64-bit. Though, key length is 64-bit, DES has an effective key length of 56 bits, since 8 of the 64 bits of the key are not used by the encryption algorithm (function as check bits only) [6]. Feistel Cipher is not a specific scheme of block cipher. It is a design model from which

1

Symmetric Encryption

57

many different block ciphers are derived. DES is just one example of a Feistel Cipher. A cryptographic system based on Feistel cipher structure uses the same algorithm for both encryption and decryption. The encryption process uses the Feistel structure consisting multiple rounds of processing of the plaintext, each round consisting of a “substitution” step followed by a permutation step. DES is in general a secure method; although it suffers minor weakness caused by complements, weak keys, design, key clustering, and differential cryptanalysis. Nobody has yet shown serious flaws in the DES, nor do people really believe that hardware power has reached the point where a brute force attack can feasibly break the DES. The problem with DES is not that it is known, or even suspected, to have been broken; it is just becoming more likely that it could be broken.

1.2.2

Triple DES

In Triple-DES, two 56-bit keys are selected. Data is encrypted via DES three times, the first time by the first key, the second time by the second key and the third time by the first key once again. This process creates an encrypted data stream that is unbreakable with today’s code-breaking techniques and available computing power, while being compatible with DES. Triple-DES needs to encrypt a singular piece of data three times before transmitting. It is CPU-intensive.

1.2.3

AES

Data Encryption Standard (DES) was the encryption standard till 2001 when was replaced by AES. The Advanced Encryption Standard (AES) was established by National Institute of Standards and Technology (NIST) in 2001 as the current standard for encrypting electronic data [7]. AES is based on Rijndael cipher which is an iterated block cipher with a fixed block length and supports variable key lengths. A block length of 128-bits and three different key sizes of 128, 192 and 256, which require 10, 12, 14 rounds respectively, are used. Figures 1 and 2 respectively shows AES ciphering and deciphering. AES is working as follows. 1. Add Round Key: Round key is XORed with the plaintext then the result will be converted into 4 × 4 matrix (state). Fig. 1 AES ciphering

Ciphering

Ciphertext

Keystream

AES core 128 bits

AES output

AES input

AES key

Plaintext Plaintext

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Wireless Communication Systems: Confidentiality

Fig. 2 AES deciphering

Deciphering

Plaintext

Keystream

AES core 128 bits

AES output

AES input

AES key

Plaintext Ciphertext

2. The Sub-Bytes transformation is a nonlinear substitution operation that works on bytes. It’s based on the Galois Field GF (28 ) with irreducible polynomial m(x) = x 8 + x 4 + x 3 + x + 1 and can be done using look up table. 3. The Shift Rows transformation rotates each row of the input state to the left, the first row will remain unshifted, the second row will be rotated by 1 step to the left whereas the second row will be rotated by two steps and the third row will be rotated 3 steps to the left. 4. Mix-Columns: Each input column is considered as a polynomial over GF (28 ) and multiplied with the constant polynomial a(x) = {03}x3 + {01}x 2 + {01}x + 02modulo x 4 + 1. The coefficients of a(x) are also elements of GF (28 ) and are represented by hexadecimal values in this equation. The Inverse Mix-Columns transformation is the multiplication of each column with a(x) = {0B}x3+{0D}x 2 +{09}x + {0E}modulox 4 − 1. 5. Key expansion.

2

Asymmetric Encryption

Many applications use asymmetric cryptography to secure communications between two parties. One of the main issues with asymmetric cryptography is the need for vast amounts of computation and storage. The concept of public-key cryptography evolved from an attempt to attack two of the most difficult problems associated with symmetric encryption. The first problem is that of key distribution, where under symmetric encryption requires either that two communicants already share a key, which somehow has been distributed to them; or the use of a key distribution center. Asymmetric algorithms rely on one key for encryption and a different but related key for decryption [5]. There are many methods for key establishment, including certificates and public-key infrastructure (PKI).

2

Asymmetric Encryption

59

The public key is freely distributable. It is related mathematically to the private key, but you cannot (easily) reverse engineer the private key from the public key. Use the public key to encrypt data. Only someone with the private key can decrypt. The key for encryption (KE ) and decryption (KD ) are different. But, KE and KD form a unique-pair. One of keys is made public, and another made private. The encryption/decryption process can be modeled by Eq. (2). P = DKD (E K E (P))

(2)

Public-key systems can be used for Encryption and Authentication. One key is used to encrypt the document; a different key is used to decrypt it. The security of asymmetric encryption rests on computational problems such as the difficulty of factorizing large prime numbers and the discrete logarithm problem. Such kind of algorithms are called one-way functions because they are easy to compute in one direction but the inversion is difficult. Public key encryption works very well and is extremely secure, but it’s based on complicated mathematics. Because of this, your computer has to work very hard to both encrypt and decrypt data using the system. In applications where you need to work with large quantities of encrypted data on a regular basis, the computational overhead means that public key systems can be very slow.

2.1

AES

The Rivest-Shamir-Adleman (RSA) scheme has been published in 1978 and since that time it is the most widely accepted and implemented general-purpose approach to publickey encryption [7]. Encryption and decryption are of the following form, for some plaintext block M and ciphertext block C: C = M e mod n

(3)

M = C d mod n = (M e )d mod n = M ed mod n

(4)

Both sender and receiver must know the value of n. Plaintext is encrypted in blocks, with each block having a binary value less than some number n. That is, the block size must be less than or equal to log2 (n). The RSA scheme is a block cipher in which the plaintext and ciphertext are integers between 0 and n − 1 for some n. The sender knows the value of e, and only the receiver knows the value of d. Thus, this is a public-key encryption algorithm with a public key of = {e, n} and a private key of = {d, n}. For this algorithm to be satisfactory for public-key encryption, the following requirements must be met:

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1. It is possible to find values of e, d, n such that M ed mod n = M for all M < n. This is done by choosing n = pq, where p and q are primes numbers. Select e, where 1 < e < (p − 1) (q − 1) and gcd ((p − 1) (q − 1), e) = 1, which means e is a prime to (p − 1) (q − 1). Select d, where d=e−1 mod ((p − 1) (q − 1)) or de=1 mod((p − 1) (q − 1)). 2. It is relatively easy to calculate mod M e mod n and C d for all values of M < n. 3. It is infeasible to determine d given e and n. An example is shown below: Assume Plaintext = 88. Key generation: n = 17 × 11 = 187. e = 7, where gcd (160, 7) = 1. D = 23, where d * 7 = 1 mod 160. Encryption process: C = 887 mod 187 = 11. Decryption process: M = 1123 mod 187 = 88.

3

Hybrid Encryption

One of the known methods to strengthen the secureness of a cryptography is by combining two existing cryptographies. A combination of two cryptographies is also called the hybrid algorithm such as combination of symmetric and public-Key based system. Symmetric key cryptography is faster and more efficient than public key cryptography, but lacks security when exchanging keys over unsecured channels. Hybrid cryptosystems combine the speed of symmetric key cryptography with the security of public key cryptography. A hybrid cryptosystem consists of a public key cryptosystem for key encapsulation and a symmetric key cryptosystem for data encapsulation. Hybrid cryptosystems are used by most computer users in the form of HTTP Secure (HTTPS) [8, 9].

4

Authentication

Authentication answers the following question “how does a receiver know that remote communicating entity is who it is claimed to be”. Authentication ensures that message has not been altered. Message is from alleged sender. Message sequence is unaltered. An example to show an authenticated email is shown in Fig. 3 where Alice wants to provide sender authentication message integrity, so Alice digitally signs message and sends both message and digital signature.

5

Crypto Analysis/Attacks

61

Fig. 3 Authenticated email

To validate the integrity of the data transmitted over the channel, Message Authentication Code (MAC) is used for checking the messages and the authentication, ensuring that the integrity of the information has not been modified under the transmission [10]. Hash functions are mathematical functions which map values in the domain values in the range. Hash functions are special mathematical functions that satisfy the following three properties: • Inputs can be any size. • Outputs are fixed. • Efficiently computable, i.e., the mapping should be efficiently computable. The purpose of a hash function is to produce a “fingerprint” of a message or data for authentication. A hash function H (m) maps an input message m to a hash value h as described by Eq. (1). Message m is of any arbitrary length. Hash h is fixed length. Often, h is called as the “message digest” of m. h = H (m)

(1)

Cryptographic hash functions are one-way function as finding hash h from m is easy, but not vice-versa. Given a message, it is hard to find another message that has the same hash value. Given a hash function, it is hard to find two messages with the same hash value. In cryptography, the avalanche effect is a term associated with a specific behavior of mathematical functions used for encryption.

5

Crypto Analysis/Attacks

The attacks to the physical layer of the wireless communication system can be classified into two categories: the passive attack and the active attack. The analysis of the cryptography algorithms in general is known as cryptanalysis and is an essential aspect of

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Wireless Communication Systems: Confidentiality

testing the reliability of the cryptography system for practical. Cryptographic algorithms are provably secure against mathematical cryptanalysis under the black-box assumption. Attacks can be passive or active. Passive Attacks do not alter or affect at any other way the information and they do not cause any issue to the communication channel. The main goal here is to acquire unauthorized access to sensitive and confidential information and data. Passive attacks are often called as stealing Information. What really makes this attack harmful is the fact that most of the times the owner is not aware that an unauthorized person has knowledge of the owner’s data. For instance, an attacker could intercept and eavesdrop a communication channel and gain knowledge to confidential information and neither the sender nor the receiver could figure that out. Active Attacks is able to process the information and alter it in many different ways. More specifically, the attacker could change specific fields of the data like the originator name, the timestamp and generally modifying the information in an unauthorized way. Moreover, unauthorized deletion of data, initiation of unintended transmission of information or data and lastly, denial of access to data by legitimate users the so-called denial of service (DoS) attack are also examples of Active attacks [11, 12].

5.1

Exhaustive/Brute-Force Attack

The secret key’s space should be long. The security of an encryption algorithm ought to have vast key space more sensitive to the secret key to tackling a different kind of attacks such as statistical attacks, differential attack, known plain text attacks and exhaustive attacks. The large size of the key space also makes brute-force attacks infeasible. Moreover, the sensitivity of algorithms towards the secret key during encryption and decryption is the key point of the robustness of an encryption algorithm. Higher the sensitivity the more secure is the information because only a slight change in the key will lead towards an entirely different cipher image. That means no one can recover the original image except having the correct secret key [13–15].

5.2

Statistical/Histogram Attack

The histogram is a common approach to get the distribution of an image pixel values. Histogram of data should be uniform after encryption. This leads to statistical attacks invalid. In information theory, entropy is the most significant feature of the disorder. We can say numerical property reflecting the randomness associated unpredictability of an information source called entropy. The ideal entropy value for a random image with a gray level of 28 is 8. Which means the closer the entropy value is, the more is the haphazardness of an image, conclusively less information disclosed by the encryption scheme.

5

Crypto Analysis/Attacks

5.3

63

Differential Attack

Attackers often make a slight change to the original data, and use the proposed algorithm to encrypt for the original data before and after changing, through comparing two encrypted image to find out the relationship between the original data and the encrypted data.

5.4

Known/Chosen Plaintext/Ciphertext Attack

5.4.1

Known Ciphertext Attacks (KCA)

Here the attacker possesses multiple cipher-texts, but without the corresponding plaintexts. This attack becomes effective when the corresponding plaintext can be extracted from one or more cipher-texts. Additionally, sometimes the encryption key can be discovered by this attack. In practice however, the adversary performing this attack have also some knowledge about the plaintext. This information could be the language that the plaintext is written or the foreseeable statistical distribution of the characters in it.

5.4.2

Chosen Plaintext Attack (CPA)

The adversary here has free access to the encryption process and can create any ciphertext from any plaintext of his choice. So basically, the attacker can have any desirable pair of plaintext-ciphertext. This makes the process of finding the encryption key easier, as the attacker can gain more knowledge of the encryption operation, the more pairs of messages and cipher-texts created.

5.4.3

Known Plaintext Attack (KPA)

This attack is quite similar to the previous one. Here the attacker knows the plaintext that the sender has sent and the corresponding ciphertext. The goal of the adversary is to gain information by taking advantage of the ciphertext-plaintext pairs they have. This could result to the discovery of the encryption key or other information for the algorithm as well. The difference with the chosen-plaintext attack is that the plaintext is not chosen by the attacker but the sender of the message.

5.4.4

Chosen Ciphertext Attack (CCA)

In this type of attack the adversary or the cryptanalyst have the ability to analyze any chosen cipher-texts along with the corresponding plaintexts. The goal is to gain the secret key or as much information as possible for the attacked cryptographic system. This attack holds with the assumption that the attacker can make the victim decrypt any encrypted message and send it to him. The more decrypted cipher-texts the attack owns the more information is gained for the system and thus it is more likely to break it.

64

5.4.5

Wireless Communication Systems: Confidentiality

Side Channel Attack

The system is attacked via the channel leaked information such as time consumption, power consumption, or electromagnetic radiation. Timing attack is one type of side channel attack. The attacker can access the equipment or physically damage them by performing, for instance, Differential Power Analysis (DPA) attack. In Differential Power Analysis, the attacker send lots of plaintext (bits) to the FPGA, which will decrypt them accordingly, and meanwhile the attacker will be measuring the power traces, trying to get the cryptography algorithm key (using statistical techniques and knowledge of the CMOS power model). There are many countermeasures against this attack, including changing the time of the key transmission or encryption to confuse the adversary and filtering the power line conditioning to prevent power-monitoring acts. Deep Learning technique can used in Side Channel Analysis Context. Like other machine learning techniques, a deep learning technique builds a profiling model for each possible value of the targeted sensitive variable during the training phase and, during the attack phase these models are involved to output the most likely key used during the acquisition of the attack traces. In side channel attack context, an adversary is rather interested in the computation of the probability of each possible value deduced from a key hypothesis. Therefore, to recover the good key, the adversary computes the maximum or the Log-maximum likelihood approach like for template attack.

5.5

Man-in-the-Middle Attack

This attack depends on standing between the two communicating parties to get the message from the sender, change and add to it and then send it forward to the receiver. This needs that the attacker knows the encryption keys to be able to encrypt the added parts to the message. One-time pad keys and changing the block lengths make the attacker not able to succeed to play the man-in-the middle role.

6

Secured Wireless Communication System

Encryption playing a vital role in any communication system. The Bluetooth encryption system uses the stream cipher E0 to encrypt the payloads of the packets which is re-synchronized for every payload. The E0 stream cipher consists of the payload key generator, the key stream genera-tor and the encryption/decryption part. The input bits are combined by the payload key generator and are shifted to the four Linear Feedback Shift Registers (LSFR) of the key stream generator. The key stream bits are then generated which are used for encryption. The Exclusive-OR operation is then performed on the key

6

Secured Wireless Communication System

65

stream bits and data stream bits to generate the ciphertext. Similarly the Exclusive-OR operation is performed on the ciphertext to get back the plaintext during the decryption process.

6.1

AES Security

WLAN encryption methods includes: • Wired Equivalent Privacy (WEP): Wired Equivalent Privacy (WEP) is a security algorithm for IEEE 802.11 wireless networks. Standard 64-bit WEP uses a 40 bit key (also known as WEP-40), which is concatenated with a 24-bit initialization vector (IV) to form the RC4 key. At the time that the original WEP standard was drafted, the U.S. Government’s export restrictions on cryptographic technology limited the key size. Once the restrictions were lifted, manufacturers of access points implemented an extended 128-bit WEP protocol using a 104-bit key size (WEP-104). • Wi-Fi Protected Access (WPA): Wi-Fi Protected Access (WPA) was the Wi-Fi Alliance’s direct response and replacement to the increasingly apparent vulnerabilities of the WEP standard. WPA was formally adopted in 2003, a year before WEP was officially retired. The most common WPA configuration is WPA-PSK (Pre-Shared Key). The keys used by WPA are 256-bit, a significant increase over the 64-bit and 128-bit keys used in the WEP system. • Wi-Fi Protected Access 2 (WPA2): WPA has, as of 2006, been officially superseded by WPA2. One of the most significant changes between WPA and WPA2 is the mandatory use of AES algorithms and the introduction of CCMP (Counter Cipher Mode with Block Chaining Message Authentication Code Protocol) as a replacement for TKIP. However, TKIP is still preserved in WPA2 as a fallback system and for interoperability with WPA.

6.2

5G Security

5G uses 256-bit encryption, a substantial improvement on the 128-bit standard used by 4G. With 5G, the user’s identity and location are encrypted, making them impossible to identify or locate from the moment they get on the network. In older 2G cellular systems, the cryptographic algorithms used to secure the air interface and perform subscriber authentication functions were not publicly disclosed. The GSM algorithm families pertinent to our discussion are A3, A5, and A8. A3 provides subscriber authentication, A5 provides air interface confidentiality, and A8 is related to A3, in that it provides subscriber authentication functions, but within the SIM card. UMTS introduced the first publicly

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disclosed cryptographic algorithms used in commercial cellular systems. The terms UEA (UMTS Encryption Algorithm) and UIA (UMTS Integrity Algorithm) are used within UMTS as broad categories. UEA1 is a 128-bit block cipher called KASUMI, which is related to the Japanese cipher MISTY. UIA1 is a message authentication code (MAC), also based on KASUMI. UEA2 is a stream cipher related to SNOW 3G, and UIA2 computes a MAC based on the same algorithm. LTE builds upon the lessons learned from deploying the 2G and 3G cryptographic algorithms. LTE introduced a new set of cryptographic algorithms and a significantly different key structure than that of GSM and UMTS. There are 3 sets of cryptographic algorithms for both confidentiality and integrity termed EPS Encryption Algorithms (EEA) and EPS Integrity Algorithms (EIA). EEA1 and EIA1 are based on SNOW 3G, very similar to algorithms used in UMTS. EEA2 and EIA2 are based on the Advanced Encryption Standard (AES) with EEA2 defined by AES in CTR mode (e.g., stream cipher) and EIA2 defined by AES-CMAC (Cipher-based MAC). EEA3 and EIA3 are both based on a Chinese cipher ZUC. While these new algorithms have been introduced in LTE, network implementations commonly include older algorithms for backward compatibility for legacy devices and cellular deployments.

7

Conclusions

This chapter discusses the fundamentals of encryption in wireless systems where encryption is classified as private and public-key cryptography. Moreover, this chapter explains the details of the main building blocks of these cryptographic systems and gives some examples for secured communication systems.

References 1. I. Ahmad, S. Shahabuddin, T. Kumar, J. Okwuibe, A. Gurtov, M. Ylianttila, Security for 5G and beyond. IEEE Commun. Surveys Tuts. 21(4), 3682–3722 (2019) 2. N. Xie, C. Chen, M. Zhong, Security model of authentication at the physical layer and performance analysis over fading channels. IEEE Trans. Depend. Secure Comput. 27 (2018) 3. P. Zhang, T. Taleb, X. Jiang, B. Wu, Physical layer authentication for massive MIMO systems with hardware impairments. IEEE Trans Ind. Informat. 19(3), 1563–1576 (2020) 4. https://www.sciencedirect.com/topics/computer-science/stream-ciphers 5. https://www.researchgate.net/publication/338556941_Single_secret_image_sharing_scheme_ using_neural_cryptography 6. K.D. Deepak, D. Pawan, Performance comparison of symmetric data encryption techniques. Int. J. Adv. Res. Comput. Eng. Technol. 1(4), (2012). ISSN: 2278-1323 7. P. Kalpana, S. Singaraju, Data security in cloud computing using RSA algorithm. Int. J. Res. Comput. Commun. Technol. IJRCCT 1(4), 145 (2012). ISSN 2278-5841 8. A.H. Vinck, Introduction to Public Key Cryptography (Duisburg-Essen, Germany, 2012). Retrieved from http://www.exp-math.uni-essen.de/~vinck/crypto/script-crypto-pdf/add-to-3. pdf. 12 May 2012

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9. J.T.J. Penttinen, 5G Explained: Security and Deployment of Advanced Mobile Communications (Wiley, 2019) 10. K.S. Mohamed, Cryptography concepts: integrity, authentication, availability, access control, and non-repudiation, in New Frontiers in Cryptography (Springer, Cham, 2020), pp. 41–63 11. B. Sun, Z. Liu, V. Rijmen, R. Li, L. Cheng, Q. Wang, H. Alkhzaimi, C. Li, Links among impossible differential, integral and zero correlation linear cryptanalysis, in Annual Cryptology Conference (Springer, 2015), pp. 95–115 12. S. Tajik, E. Dietz, S. Frohmann, J.-P. Seifert, D. Nedospasov, C. Helfmeier, C. Boit, H. Dittrich, Physical characterization of arbiter PUFs, in International Workshop on Cryptographic Hardware and Embedded Systems (Springer, 2014), pp. 493–509 13. C. Li, D. Lin, B. Feng, J. Lü, F. Hao, Cryptanalysis of a chaotic image encryption algorithm based on information entropy. IEEE Access 6, 75834–75842 (2018) 14. X. Chai, Z. Gan, K. Yang, Y. Chen, X. Liu, An image encryption algorithm based on the memristive hyperchaotic system, cellular automata and DNA sequence operations. Sig. Process. Image Commun. 52, 6–19 (2017) 15. Y. Zhang, The image encryption algorithm based on chaos and DNA computing. Multimedia Tools Appl. 77(16), 21589–21615 (2018)

Wireless Communication Systems: Reliability Channel Coding, Error Detection and Correction, Equalization, Diversity

In wireless communications, data can be corrupted due to radio interference or electrical noise. So, it is useful to have a way to detect and correct such data corruption. To detect or correct errors on digital communications channels, error-control coding techniques are employed. Errors can happen for 1 bit or n-bits and in this case it is called a burst error. The bits of the data may change (either 0 to 1 or 1 to 0). Error detection and correction code plays an important role in the transmission of data from one source to another. In this chapter, the management of errors in the received data stream by means of forward error correction and re-transmission are covered. Moreover, it introduces the channel codes such as the LDPC, the polar and the turbo codes. It presents the corresponding encoding and decoding block for each channel code with in-depth details. Multipath Propagation causes fading. Fading causes bit errors. The most effective techniques to reduce the effects of multipath are Equalization and channel coding. Diversity improves reliability of a message signal by using two or more communication channel with different characteristics. Diversity plays an important role in combating fading interference and avoiding channel errors.

1

Introduction

The big picture for error detection and correction is shown in Fig. 1. Channel coding provides trade-off between throughput, reliability, and delay [1]. All these algorithms will be discussed in the subsequent sections.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 K. S. Mohamed, Wireless Communications Systems Architecture, Synthesis Lectures on Engineering, Science, and Technology, https://doi.org/10.1007/978-3-031-19297-5_4

69

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Wireless Communication Systems: Reliability

Fig. 1 Error correction and detection algorithms

2

Error Detection Techniques

2.1

Checksum

For error detection by checksums, data is divided into fixed sized frames or segments. The sender adds the segments using 1’s complement arithmetic to get the sum. It then complements the sum to get the checksum and sends it along with the data frames.

2.2

Parity Check

Parity Checking of Error Detection It is the simplest technique for detecting and correcting errors. The MSB of an 8-bits word is used as the parity bit and the remaining 7 bits are used as data or message bits. The parity of 8-bits transmitted word can be either even parity or odd parity.

2.3

CRC

CRC is one of the most powerful error-detecting codes [2–10]. The conventional serial CRC method is very slow and using it will sacrifice the low latency and concurrency provided by this protocol. There are some other methods to implement parallel CRC32 for fast operation [11]. Three methods are presented to realize parallel CRC for HMC. Method 1 is based on using symbolic toolbox of MATLAB, method 2 uses an existing tool to generate Verilog code for the CRC but with a modification to support maximum block size for data. Method 3 uses long division method to calculate the CRC32.

2

Error Detection Techniques

71

G(x) =X16+X12+X5+1

Da ta _In +

CRC_Out D

Q

+

Q

D

+

D

Q

Initia l

Bit S hifte d in 0:

Bit S hifte d in 1:

Bit S hifte d in 7:

15:C15

15:C14

15:C13

15: C7 ^ D4 ^ C11 ^ D0^ C15

14:C14

14:C13

14:C12

14: C6 ^ D5 ^ C10 ^ D1^ C14

13:C13

13:C12

13: C11^D0 ^ C15

13: C5 ^ D6 ^ C9 ^ D2 ^ C13

12:C12

12:C11 ^D0 ^ C15

12: C10^D0 ^ C15

12: C4 ^ D0 ^ C15 ^ D7^ C8 ^ D3 ^C12

11:C11

11:C10

11:C9

11: C3 ^ D1 ^ C14

10:C10

10:C9

10:C8

10: C2 ^ D2 ^ C13

9:C9

9:C8

9:C7

9: C1 ^ D3 ^ C12

Fig. 2 CRC16 example to illustrate the idea which is applicable to any CRC type for method 1

A. Method 1: Symbolic Toolbox-Based Method Linear feedback shift registers handles the data in a serial manner as shown in Fig. 2. From the linear feedback shift register (LFSR) operation, symbolic toolbox, in MATLAB are used to generate the final equations of the CRC, and then theses equations are exported to VERILOG so that we are able to calculate it in only one clock cycle. The final expressions are in the form of XOR matrix. Noting that number of iterations is equal to the input data widths. B. Method 2: Existing Web-Based Tool with Limitation This method depends on using an existing web-based tool, [12], that can generate parallel CRC32, but this tool has a limitation on the input data width as it is less than the maximum allowed data width in HMC which is 1152 bits, so we were able to find a work around method that enables us to calculate large data width with this tool. This work around is based on the linearity property of CRC where, CRC is a linear operation:  CRC(A + B) = (1) (CRC(A) + CRC(B)) So, we divide the message into two parts and the CRC of the first stage is an input to the second stage of the CRC and the final CRC is the output from the second stage. This method can be applicable to any data length.

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Wireless Communication Systems: Reliability

B1

Start

M={B1,B2}

Parallel CRC32

B1=1024 bit

CRC32_Tmp

Parallel CRC32

CRC32_Final

B2=128 bit

B2

Fig. 3 Method 2 to calculate CRC32

The latency is only two clock cycles as depicted in Fig. 3, which is better than 1152 cycles in case of Conventional serial CRC. C. Method 3: Long Division-Based Method A CRC is a popular error detecting code computed through binary polynomial division. To generate a CRC, the sender treats binary data as a binary polynomial and performs the modulo-2 division of the polynomial by a standard generator. The remainder of this division becomes the CRC of the data, and it is attached to the original data and transmitted to the receiver. So, the proposed method here is based on Polynomial arithmetic, where the Golden rule is modulo-2 (xn + xn = xn − xn = 0). Assuming that we want to calculate R which is the CRC for message M, so we have the following equation (Fig. 4): T = M 2r + R

(2)

Choose a generator string G (r + 1 bits) so that, T = M.2r + R = A.G

(3)

In Case of HMC: G = X32 + X30 + X29 + X28 + X26 + X20 + X19 + X17 + X16 + X15 + X11 + X10 + X7 + X6 + X4 + X2 + X + 1

Fig. 4 Method 3 for calculated CRC32, which is based on polynomial long division

(4)

M (info bits)

R (Check bits)

K Data bits

r Check bits T (codeword) =M2r+R

3

Error Correction Techniques

73

So adding R to both sides of Eq. (3) results in: M.2r = A.G + R

(5)

So, the CRC is the reminder of division of M.2r and G. Extended Euclidean algorithm, [13, 14], which is used to find the greatest common divisor of two integers, can be extended to calculate this long division problems. If we keep track of a bit more information as we go through the algorithm, we can discover how to write the greatest common divisor as an integer linear combination of the two original numbers. In other words, we can find integers s and t such that: gcd(a, b) = sa + tb

(6)

From the above equation we can calculate the CRC as the reminder. The latency for this algorithm is 37 clock cycles, still higher performance than the conventional serial CRC calculation method. Table 1 summarizes some of the pattern tests and the resulting CRC32.

3

Error Correction Techniques

Error correction can be handled in two ways: 1. Backward error correction (retransmission). 2. Foreword error correction.

3.1

Backward Error Correction Techniques

When error is discovered by the receiver then sender retransmits the entire data unit to the receiver, this is called backward error correction. This is an error control technique whereby an error detection scheme is combined with requests for retransmission of erroneous [12–21]. Automatic repeat request (ARQ) is another error management technique, in which the transmitter takes a block of information bits and uses them to compute some extra bits that are known as a cyclic redundancy check (CRC). It appends these to the information bits, and then transmits the two sets of data in the usual way. The receiver separates the two fields and uses the information bits to compute the expected CRC bits. If the observed and expected CRC bits are the same, then it concludes that the information has been received correctly and returns a positive acknowledgement (ACK) to the transmitter. If the CRC bits are different, it concludes that an error has occurred and returns a negative acknowledgement (NACK). On receiving a NACK, the transmitter sends the

74

Wireless Communication Systems: Reliability

Table 1 Different patterns and corresponding calculated crc32 Pattern (1152 bits)

CRC32

b4 79 a5 65 c2 68 e6 e8 45 d5 7c 4d 22 b0 6a 62 da 56 ec 56 7c 46 47 70 3d e7 d0 fb9e00c8 4d a9 0f d9 7f ea 49 84 77 a9 b7 4b 82 dc 45 d7 f4 8a 12 0f 3e 14 74 d7 d6 d3 c4 d8 39 68 a0 a9 fd 88 d0 48 3a 7d 6f 56 47 a9 e7 29 ce a0 f9 24 19 1d 9b bd 03 d8 b6 b1 31 ae 20 ca 88 c7 f5 fc 84 e3 3d 7c 6c 6f 9b 6e 23 5c 30 8a b9 aa 60 37 4c 14 de 88 57 88 de c0 ee 11 ae 02 8e 9b d8 ea 0d c3 43 8f 3d 81 44 b9 6d 95 0d 64 6c b7 a0 65 49 df d1 c4 92 9d 6b 7e cf 64 b5 85 5c 25 42 1a c2 ef 9c 5c 3e 53 b2 1f e1 5e eb 0d dc c6 f8 1c 2088111902 b4 6f e7 8f a4 54 44 c9 cb 20 29 48 c4 a4 a8 1b eb fb 58 10 80 f3 6a c3 d5 a5 58 55 9e 86 d0 b3 b0 2a 94 18 27 5b 5a af 24 0e aa 67 41 31 28 12 06 59 e1 3f 9d 51 50 70 ac 14 36 83 32 eb 12 f7 a6 a4 c3 66 1f b5 16 0a 4b 74 80 88 45 94 d6 8f e7 0a 55 2a b2 87 4a 0d 27 a6 cf 33 9c 96 63 7c 3c 7b 5c a2 be a6 ac 20 c5 61 08 54 7e a1 05 e2 c9 5b 00 30 74 24 35 08 6e fd 10 26 89 e4 c2 b3 7c dd 72 7c 78 42 8d b0 85 17 d3 ca c8 bc a6 f1 75d28a71 14 a3 38 1a d8 c8 a6 39 26 f6 f8 68 9f 66 ff ce 9a bc 6e 43 6d d1 33 da 15 29 28 f6 f7 d8 c3 02 18 b5 59 89 ef 37 22 11 2a 8f c0 d9 15 e6 38 d0 24 0d c1 8e e2 be ff cb 68 c5 4a 90 c3 9d a4 52 89 b6 48 90 ce e7 58 53 94 24 f7 7c c8 07 3d 67 18 59 f0 4a 07 11 ae a4 96 c0 1b b8 58 14 aa b5 79 86 4e 65 e2 51 e1 b8 7c 94 9c 85 ee 5e 78 ba 04 b7 32 b4 dd 38 9e 4c 29 24 53 26 ec e4 df 50 8c 6a 3e de 00 7a 16 3f 53 9f 6e 6a b3 1a 92 98 69e0f3a1 b2 e2 fc 85 22 6a fa 25 70 0c af d8 e6 f7 62 19 ec 91 a4 36 ec 0e f5 ee ee 16 9a 94 d8 eb 95 a5 fd 34 38 bf 40 16 ac 11 11 5c 1e e5 49 f6 8a a2 d1 22 47 31 b3 f5 ba 81 14 28 75 2e 60 cd de 6a 4e 09 a6 28 6e 84 cc 82 cc d6 d8 24 da 75 7f f5 5c d3 12 82 47 10 4f aa 4f 5c 8f 07 f8 65 e4 19 8d 61 d2 8e 56 3e 37 b7 74 4e 58 e3 e7 e0 b7 aa 86 5b 88 ed 05 11 70 dc 29 10 b8 01 9a a7 c2 66 36 0a 17 5f 5e 91 31 00 55 2d cb 78 82 4c 6d 66 59207f16 92 87 1d a5 3c f9 bc b4 2e ed 0a c4 07 81 d5 f6 23 13 4b b3 61 e3 71 8c d0 62 a5 f0 2a 47 12 72 b0 7e be 34 ce f9 3f 8f 38 73 b5 e7 80 01 05 57 b7 3e 22 bd 7e c1 d6 59 64 f1 6e f8 26 4e 7a 95 f9 1d dd d3 ae 99 44 af f4 6e 27 64 de 50 5d 63 0a b2 99 e2 ed 7e bc 93 db e9 fd 58 ad e6 f3 a6 10 5b 29 79 28 74 87 cf c3 99 fb 7f 36 26 a6 3f 21 80 cc e5 a6 4d c9 1e 8c 99 65 68 82 6a cd 5e 2f a6 7b 06 cb 8a 9f 48 3c 90 56 58 88 f1 f2 08 7fecf575 f6 8f 72 4d 51 de aa 17 5a ee 55 8c 39 97 b7 0d ac 86 e7 1e 99 ad 6d eb 74 3b 9e 26 ac 88 3f d4 38 70 d2 ba 9c 0d 67 98 0c d1 d8 a5 83 3c 73 fa 9b 7e 6b fd a4 fe f7 c4 b1 17 eb da e1 fb 61 a4 d0 77 25 9b 33 a6 fb 71 db 6b 7a 60 83 0b bd a4 98 a0 ed 06 4b 0b ec 40 e0 5b 72 59 71 a2 89 a6 3b 85 d5 7e df 8e e2 33 c7 81 30 c1 86 80 de a1 c5 a9 ce 0a 31

original block of information again, and the process continues until the information arrives correctly [75, 76].

3

Error Correction Techniques

3.2

75

Forward Error Correction Techniques

Classic FEC codes include algebraic codes such as Hamming, and Reed-Solomon codes. All of these codes involve the introduction of redundant data which allows the received code to be correctly decoded despite some level of errors or loss in the transmission over a noisy channel. In this method, error correction receiver can use an error-correcting code which automatically correct certain errors, this is called forward error correction. The most famous types are hamming code and convolutional code (Viterbi decoder). Moreover, interleaving can be used to reduce errors.

3.2.1

Hamming Code

Hamming code is one of the error correction techniques is used to correct the single bit error. Hamming distance between two sequences = Number of bits in which they disagree. Hamming code works as follows:

• At transmitter side Assume that we have a data message of size k, we add some parity bits to this message to be able to detect error location at receiver side. The parity bits size is of size p. So, the total size of the message to be sent is n, where n = k + p. To generate these parity bits, we multiply the data message by a certain matrix. The equations for this operation can be shown below: c = d.G

c d G

is the data to be sent. is the data before applying the ECC. is the ECC generator Matrix at transmitter side.

To determine p, we should satisfy the following equation: K + p + 1 ≤ 2p • At Receiver side It receives a message of size n. it extracts the p bits from it. It starts checking these bits by multiplying them by a certain matrix to make sure that the output equal “0”. If the output equals any other value, so this values indicates to the error location. For example,

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Wireless Communication Systems: Reliability

if the value is “2”, so the error in bit number “2” in the data message and we can flip the bit to correct it. The equations for this operation can be shown below: s = H T .c

c S H

is the data to be sent. At the receiver side, it is the received data. is the calculated syndrome. This value should be zero for error-free data message. is the ECC parity-check Matrix at receiver side. Construction of G and H   G := Ik | − AT H := (A|In−k ) G and H must satisfy: HGT = 0

The order of the columns of matrix A does not matter. A Matrix determine which parity bits check which positions. • An Example: Hamming code C (7,4) At TX side: Datawords

Codewords

Datawords

Codewords

0000

0000000

1000

1000110

0001

0001101

1001

1001011

0010

0010111

1010

1010001

0011

0011010

1011

1011100

0100

0100011

1100

1100101

0101

0101110

1101

1101000

0110

0110100

1110

1110010

0111

0111001

1111

1111111

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Error Correction Techniques

77

At RX side: it can detect the error even it happens in the parity bits. Syndrome

000

001

010

011

100

101

110

111

Error

None

b0

b1

b2

b3

b4

b5

b6

Burst error correction using hamming code: Sender

Receiver (with burst error)

0

0

0

1

1

0

1

0

0

0

0

1

1

Codeword 4 0 1

0

0

0

1

1

0

0

Codeword 3 0 1

1

0

1

1

1

1

Codeword 2 1 1

1

1

1

Codeword 1

1

0

0

1

1

Codeword 4 0 0

0

0

0

0

Codeword 3 1 1

1

0

1

1

Codeword 2 0 1

1

1

Codeword 1

A data unit transmits 0 0 1 1 0 1 0 1 0 1 0 1 1 0 1 0 0 0 1 1 0 0 1 1 1 0 0 1 Corrupted bits

The above figure shows that when a burst error of size 4 corrupts the frame, only 1 bit from each codeword is corrupted. The corrupted bit in each codeword can then easily be corrected at the receiver. With the addition of an overall parity bit, it can also detect (but not correct) double-bit errors.

3.2.2

Linear Block Codes

Linear block codes are used for error detection and correction. But nonlinear block codes are not commonly used as their structure makes theoretical analysis and also it is difficult to implement. A linear block code is a code in which the EXCLUSIVE OR of two valid codewords creates another valid codeword. Table 2 shows a list of datawords and codewords. This shows a linear block code because the result of XORing any codeword with any other codeword is a valid codeword. For example, the XORing of the second and third codewords creates the fourth one.

3.2.3

BCH Code

For any positive integers m ≥ 3, t < 2m−1 and k is the number of the data bits, there exists a binary BCH code with the following parameters:

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Wireless Communication Systems: Reliability

Table 2 Datawords and Codewords of linear block codes

Datawords

Codewords

00

000

01

011

10

101

11

110

• Block length (code-word): = 2m − 1. • Number of parity-check digits: n − k ≤ m ∗ t. • Minimum distance: dmin ≥ 2t + 1. BCH code is a generalization to the hamming code (BCH). It can correct any number of errors, and when the number of errors is equal to 1 it reduces to the hamming code-word length, hamming distance and correctable digits all follow the equations above. Some examples (using above equations, and letting m = 4, so n = 15): 1. Minimum Hamming distance of dmin = 3 and corrects up to t = 1 error. This code has k = 11 data bits and n − k = 4 checksum bits (normal hamming code). 2. Minimum Hamming distance of dmin = 7 and corrects up to t = 3 errors. This code has k = 5 data bits and n − k = 10 checksum bits. The problem with it is that for relatively large number of correction digits or hamming distance, the number of the extra bits of the check bits is so large, it can even be larger than the original word length, as shown in the examples above, that makes it non-power efficient [22–26].

3.2.4

Reed Solomon Code

The Reed Solomon codes are the most implemented codes in numerous applications especially in storage systems and Satellite communications. The Reed Solomon codes relies on the polynomials of one variable (univariate polynomials). By adding t parity bits, The Reed Solomon code is able to detect up to t errors. In addition, the code is also able to correct up to 2t errors. Furthermore, the code could correct up to t erasure errors. The number of parity bits is left to the designer based on the required application and the importance of the handled data. It will be advisable to increase the number of parity bits in order to correct as many errors as possible, but it will not be the practical solution to reduce the number of information bits (n − t) as it will increase the delay of transmitting the information as well as the complexity of decoding. Therefore, it has to be a multidimensional problem that takes care of power, speed, delay, complexity etc.

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Error Correction Techniques

79

The Reed Solomon code is considered as a linear code. Therefore, the hamming distance is defined as (n − k + 1) positions. The code with m message symbols and n code symbols given the message vector [13, 14]: [x1 x2 . . . xm ]. Let P(t) to be the polynomial P(t) = xm t m + xm−1 t m−1 + · · · + x2 t + x1 . Then the code-words is defined as the following set: S = {P(x1 ), p(x2 ), . . . , P(xm )} the Reed Solomon code use the following enumeration:   (x1 , x2 , . . . x N ) = 0, β, β 2 , . . . β N −2 , 1

(1)

(2)

If the received word is: (R(x1 ), R(x2 ), . . . , R(x N )); N equations might include some errors. R(0) = m 1 R(β) = m 1 + m 2 β + m 3 β 2 + · · · + m m β m−1   R β 2 = m 1 + m 2 β 2 + m 3 β 2∗2 + · · · + m m β 2∗(m−1)

(3)

R(1) = m 1 + m 2 + m 3 + · · · + m m Assuming the are at most e errors such that Ri  = P(i ). Then those polynomials exist: E(t) o f degr ee < e Q(t) o f degr ee < m + e − 1 Then, the key equation for the decoding algorithm. The key equation needs to be solved by interpreting it as a system of linear equations for the unknown coefficients of the polynomials E(t) and Q(t). The decoding algorithm is repeatedly produce potential polynomials, until a sufficient number of matching polynomials are produced to reasonably eliminate any errors in the received code-word. Then, the polynomial is determined, so any errors could be corrected by recalculating the corresponding code-word values. Therefore, the algorithm only works on simplest cases and impractical on largest cases. For example, the Reed Solomon (255, 239) can correct up to 8 errors only. The architecture of a traditional RS decoder is shown in Fig. 5. The decoder is typically composed of five subsystems: syndrome calculation, error polynomial calculation, error positions extraction, error magnitude evaluation, and error compensation. Once a message is received by a Reed–Solomon decoder, the first step should be to divide the received polynomial by the generator polynomial chosen for encoding: the remainders of this division are known as syndromes, and they do not depend on the transmitted code word, but rather only on errors. The syndrome calculation block computes the 2t syndromes contained into a Reed–Solomon code word, usually exploiting Horner’s method.

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Wireless Communication Systems: Reliability

Fig. 5 Conventional RS decoder

The next step is to introduce the error locator polynomial that contains the information about the location of the errors and their magnitudes. Two methods are widely used in error polynomial calculation, the Euclidean algorithm and Berlekamp’s algorithm. Once the coefficients of error location polynomial are carried out, the error position block identifies the corrupted symbols, while the error magnitude block computes the error values. Finally, the error compensation block uses this information to fix the errors [27–29].

3.2.5

Low Density Parity Check (LDPC)

LDPC codes are widely used in different standards such as Wi-Fi, WiMax, and DVBS2. LDPC was invented in early 1960s. LDPC is a linear code that could correct up to . LDPC codes are used to send information over noisy channels. It’s also the e = (n−k−1) 2 most FEC code that could reach the theoretical limit by adjusting the threshold values of the noise. That’s why LDPC codes are the closest codes to the theoretical limit of Shannon theory. LDPC is built using the advantage of a sparse parity check matrix at the beginning. Then, the generator matrix is determined. That is why they are more practical during decoding. LDPC consists of two important matrices [30, 31]: (1) Parity Check Matrix (H): contains all the equations designed to check the parity. Its (r ∗ n) matrix; where (r) is the number of equations (rows) and (n) is the column binary matrix as shown in Eq. (4). ⎡ c1 ⊕ c2 ⊕ c4 = 0 1 ⎢ c2 ⊕ c3 ⊕ c5 = 0 ⎢0 →H=⎢ ⎣1 c1 ⊕ c5 ⊕ c6 = 0 c3 ⊕ c4 ⊕ c6 = 0 0

1 1 0 0

0 1 0 1

0 0 0 1

1 1 0 0

0 0 1 0

⎤ 0 ⎥ 0⎥ ⎥ 1⎦ 1

(4)

A graphical implementation is very supported by LDPC instead of making complex math relations. The graph is known as Tanner graph. The graph is formed using two kinds of nodes. The first one is the check node that represent the parity check equations. The number of the check nodes is equal to the number of rows in (H). The other type is bit

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Error Correction Techniques

81

nodes which represent the received bits (n). The edges connect each check nodes with its corresponding bit nodes. Each check node receives the value of bit nodes and decides whether the parity check equation is zero or not. If the result is zero, the condition is said to be satisfied [32] (Fig. 6). (2) Generation Matrix: The generation matric contains all the constraints in order to encode the message besides [33]. The generator matrix can be derived from the sparse matrix performing Gauss-Gorgan Elimination to be in the following form. 

H = A, In−k

(5)

where A(n − k) ∗ k is a binary matrix and I (n − k) is the identity matrix. The generation matrix is therefore defined as   (6) G = Ik, A T

That is why, the G matrix is orthogonal to H, as: GHT = 0

(7)

The LDPC algorithms are divided into two kinds: (a) Hard Decision (bit flipping algorithm): The binary decision for each received bit is made at the detector and moves to the decoder. The tanner graph is easily used to check the received message. Each bit node sends its value to the related check nodes. Then, the check nodes send back what is the expected value for the bit nodes based on the received information from other bit nodes. The check node checks if the parity check equation is satisfied. If result is not zero and the majority of messages received by the bit nodes suggests the opposite value of the node, the bit node changes its current value to the opposite one. Then, all check nodes, parity check equations, is tested again till it is satisfied. Fig. 6 The Tanner graph represent the H matrix. The check nodes equals the number of rows and the parity check equations. The bit nodes equals the number of received bits

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Wireless Communication Systems: Reliability

This operation occurs only when the check nodes is not satisfied. However, if the received code word is correct, the previous operation will finish immediately. Therefore, there will no additional processing steps once the check nodes is satisfied. (b) Sum-Product algorithm: The Sum-Product is a soft algorithm for the received messages. Unlike the bit flipping algorithm, the soft algorithm depends on the probability of the received of being either 1 or 0 instead of saying it’s a perfect 1 or 0. The input bit probability is called a “priori” because it was known from the channel’s probability to cross over at the receiver before running LDPC. However, the probabilities resulted from the decoder is called a “posteriori” probability. The algorithm depends on the Log Likelihood to specify the value of the received of bits. For the binary values of (x): P(x = 1) = 1 − P(x = 0)

(8)

Therefore, one probability is required to be saved. The binary matrix id determined by the Log Likelihood concept as:   P(x = 0) (9) L(x) = loge P(x = 1) If P(x = 1) < P(x = 0), then, the L(x) is a positive number meaning that the x value is zero and vice versa. Furthermore, the higher the difference between the two probabilities, the closer the value to be a perfect 0 or 1 based on the sign of L(x). The advantage to use Log is the ability to add the probability instead of multiplying them. This results in more simple hardware implementation. The Goal of the Sum-Product algorithm is to calculate the maximum a posteriori probability for each bit node based on the principle of conditional probability. Pi = P{ ci = 1|N }

(10)

The previous equation measures if the i-th probability bit is (1) given that all parity bit equations are satisfied that is said to be event (N). While the input value is said to be an extrinsic information. The extrinsic information is independent from the priori probability. The generation of the H matrix requires only the input Log Likelihood ratio for each i-th element. The result would be the a posteriori probability as Log Likelihood ration too. Iterative decoding can be used as a decoder for LDPC [34].

3

Error Correction Techniques

3.2.6

83

Convolutional Code

Convolutional codes encode the data bits by generating more than one encoded codeword for it via algebraic manipulation. They have different types, including systematic nonrecursive, nonsystematic non-recursive and systematic recursive. Systematic means that one of the outputs is the input itself. Recursive means that at least one of the outputs has feedback from the code block. The number of the inputs over the number of the outputs is called the rate of the convolutional code [35]. For instance, the three circuits shown in Fig. 7 are with rate ½, meaning that each one of them has one input and two outputs, with the first one being systematic non-recursive, the second one being nonsystematic nonrecursive and the third one being systematic recursive. The illustrated 8-bit rate ½ codes use octal representation to give details about the generator circuits designs by representing each copying element with ‘1’ for each output if it participates in the addition modulo 2 process and ‘0’ for each output if it is used only to copy the input bits to the following copying element without participating in the addition modulo 2 process. The resultant tap vectors are then converted into octal representation. Systematic codes are distinguished with that one of its tap vectors is 1. Recursive codes are distinguished with that they have the output branch vector with feedback divided by the feedback branch vector. A Convolutional encoder consists of one or more shift registers and multiple XOR gates. A Convolutional encoder typically will generate two or three output bits for each input bit. The output bits generated by the encoder are dependent on the current input bit, as well as the state of the encoder. The state of the encoder is represented by several bits which precede the current bit. If the state of the encoder consists of the three previous bits, then there are eight possible encoder states, one for each possible combination. This encoder is said to have a constraint length K = 4 since the output depends on four bits (the current bit plus three previous bits). The code rate r is defined as the number of input bits divided by the number of output bits. Thus, an encoder which produces two output bits for every input bit is said to have rate r = ½ [16]. Convolutional coding adds redundant bits in such a way that the decoder can, within limits, detect errors and correct them. Before the information bits are encoded, four bits are added at the end of the information bits. These bits are all set to zero and are used to reset the Convolutional encoder to its initial state. In general, the performance of a Convolutional encoder will improve as the constraint length increases, or as the code rate decreases. As in the example in Fig. 7 the encoder generates three bits for each input bit and the main parameters of convolutional encoder can be summarized in Table 3 whereas generator corresponding to the shift register connection to the upper and lower XORs (these generators patterns was found to be the best). Constrain length corresponding to how many l-bit stages are available to feed the combinational logic that produce the output symbols [17]. As for the decoding mechanism for error correction, the convolutional decoders find the a posterior probability of each bit using the sum-product algorithm (also known as forward-backward or BCJR algorithm). The most probable bit state is found by the min-sum algorithm (also known as Viterbi algorithm). Convolutional codes have many

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Wireless Communication Systems: Reliability

G0 + DATA

M3

M2

M1

M0

+

G2

Fig. 7 Convolutional encoder

Table 3 Parameters of convolutional encoder Definition

Symbol

Value

Input number

k

1

Output number

n

2

Encoder rate

k/n

1/2

Constrain length

l

4

Generator sequence

g

[1 0 0 1 1] [1 1 1 1 1]

advantages. For instance, they are fast, efficient and generally accurate. In addition, they have good approach to Shannon’s theoretical limit. However, they also suffer from a major disadvantage. The Hamming distance of convolutional codes differ for different code-words, and some specific code-words have Hamming distance of one, which makes error correction unreliable for such code-words. The Viterbi algorithm is commonly used in a wide range of communications and data storage applications. It is used for decoding convolutional codes, in baseband detection for wireless systems, and for detection of recorded data in magnetic disk drives. The requirements for the Viterbi decoder depend on the application in which it is used. This results in a very wide range of required data throughputs and may impose area or power restrictions. The Viterbi detectors used in cellular telephones have low data rates (typically less than 1 Mb/s) but must have very low energy consumption. On the opposite end of the scale, very high speed Viterbi detectors are used in magnetic disk drive read channels, with throughputs over 600 Mb/s but power consumption are not as critical. The Viterbi decoder is the most important part in the receiver; the Viterbi algorithm is one of the most common decoding algorithms used for decoding convolutional codes. To understand Viterbi’s decoding algorithm, it is convenient to expand the state diagram of the encoder in

3

Error Correction Techniques

85

time, i.e., to represent each time unit with a separate state diagram. The resulting structure is called a trellis diagram. A trellis diagram is an extension of a convolutional code’s state diagram that explicitly shows the passage of time where the branches of the trellis diagram are labelled with the output bits corresponding to the associated state transitions [18, 19]. Assuming that the encoder always starts in state S0 and returns to state S0, the first m time units correspond to the encoder’s departure from state S0, and the last m time units correspond to the encoder’s return to state S0. It follows that not all states can be reached in the first m or the last m time units. However, in the center portion of the trellis, all states are possible, and each time unit contains a replica of the state diagram. There are two branches leaving and entering each state. The upper branch leaving each state at time unit i represents the input ui = 1, while the lower branch represents ui = 0 [20]. The convolutional encoder shown in Fig. 8 has four shift registers so we have 16 states and the trellis diagram of the convolutional encoder is splitted and shown in Figs. 9 and 10.

Fig. 8 State diagram of the convolutional encoder

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Wireless Communication Systems: Reliability

Fig. 9 Trellis diagram of the convolutional encoder (part 1)

3.2.7

Turbo Code

Turbo codes are developed by combining more than an encoder, usually convolutional encoders. Each encoder receives a permuted version of the data bits input through the use of an interleaver. The way the encoders take their permuted version of their inputs determines the turbo encoder type [36, 37]. In parallel concatenated convolutional code (PCCC) structure as shown in Fig. 11, the encoders take different permuted versions of

3

Error Correction Techniques

87

Fig. 10 Trellis diagram of the convolutional encoder (part 2)

the information bits input and operate in parallel, and their outputs are concatenated and combined to form the final codeword. In serial concatenated convolutional code (SCCC) structure as shown in Fig. 11, the outputs of one encoder become the inputs of another after passing through an interleaver, and thus the encoders operate in a serial fashion. The structure could also by hybrid (HCCC) using parallel and serial structures together to optimize the performance, as shown in Fig. 12 [38–63].

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Wireless Communication Systems: Reliability

Fig. 11 Convolutional codes rate 1/2 examples using linear shift registers and addition modulo 2 blocks. a Systematic nonrecursive code. b Nonsystematic nonrecursive code. c Systematic recursive code

Fig. 12 Serial convolutional concatenated turbo encoder structure

3

Error Correction Techniques

89

Other types of turbo codes do not need the encoder to necessarily be of convolutional structures. Block turbo codes use block encoders for higher code rates (typically more than 0.7) and higher spectral efficiencies. In addition, Block turbo codes performance does not depend much on the interleaver’s design. Pyndiah et al. [46] reported that for spectral efficiencies higher than 4 bits s−1 Hz−1 , block turbo codes performance was higher than convolutional turbo codes. Another type of turbo codes uses trellis coded modulation (TCM) concepts and are called turbo trellis coded modulation (TTCM). The usage of TTCM show better performance than convolutional TCM, but the implementation is complicated. Furthermore, simulation results for the BER show an error floor that cannot be lowered than 10−6 . The interleaver is part of the turbo encoder which manipulates the sequence of the resultant codewords to give different combinations, and its design is the main factor that determines the turbo code performance especially for convolutional turbo codes. There are many types of turbo codes interleavers: 1. Row-column interleaver: the bits are written in a memory in a row-wise fashion and read in a column-wise fashion. 2. Helical interleaver: the bits are written in the interleaver’s memory in a row-wise fashion and read in a diagonal fashion. 3. Odd-even interleaver: if this interleaver is used, the un-interleaved information bits are encoded in the first encoder, and only the odd bits are stored. The interleaved bits are encoded in the second decoder, and only the even bits are stored. By multiplexing the two outputs, the codeword is formed and transmitted into the channel. This interleaver’s design has the constraint that each information bit corresponds to exactly one coded bit from either one of the encoders, or else the decoder will have bad performance with the un-coded bits in both encoders ‘dimensions’. 4. Simile interleaver: for this type, the encoders have to end at the same ‘zero’ state they started at. Therefore, a ‘tail’ bit is appended to the information bits so as to return the state of the encoders to their zero state. 5. Frame interleaver: this type of interleavers uses a generator polynomial of period L. The interleaved bits are stored in a memory of twice their size. The target is to choose the addresses of the information bits storage such that their successive readings make the encoder’s final state return to its zero state without the need for a ‘tail’ bit as in the simile interleaver. 6. Pseudo-random interleaver: the order of the interleaved bits is chosen pseudorandomly with no criterion except for computer simulations. 7. S-type interleaver: the order of the interleaved bits is chosen “S-randomly”, which means that for each permutation created pseudo-randomly, it is compared to S previous permutations, where S > 0. This is performed to avoid identical permutations, which may result in the case of purely pseudo-random interleaver.

90

Wireless Communication Systems: Reliability

8. Uniform interleaver: a conceptual interleaver in which represents the average of  all  n possible interleavers, where the message of k ones and n − k zeros maps to k permutation with equal probabilities. For many classes of turbo codes, the code performance is very dependent on the interleaver’s design, therefore choosing the interleaver that best suits the application at hand is of high priority. There are multiple decoder algorithms that could be used for turbo codes. The optimal decoding algorithm for turbo codes is the maximum likelihood (ML) especially applied for the turbo trellis structure. However, the presence of the turbo code interleaver makes the number of possible states very huge, thus making the practical usage of ML algorithm with turbo decoders almost impossible. Therefore, alternative decoding algorithms are used. Figure 12 gives an example for a generic turbo code decoder, based on the concept of iterative decoding. The algorithm implementing the DEC blocks could be chosen of a variety of algorithms. For instance, a soft decision decoder could be used, where a real number is outputted to represent the a posteriori probability (APP) of a correct decoding decision. Two types of soft decision decoders are the soft output Viterbi algorithm (SOVA) and the maximum a posteriori (MAP) algorithm. The complexity of the MAP algorithm is twice the complexity of the Viterbi algorithm, but it gives better performance at low SNR, as it evaluates the APP with more accuracy. Other decoding algorithms include the soft in soft out (SISO) algorithm, which is easier to use than the MAP algorithm when used with parallel paths between two states in a given trellis diagram. Another decoding algorithm useful with reducing the error floor of the turbo codes is the list output Viterbi algorithm (LOVA), which is similar to the soft list Viterbi algorithm (SLVA). To reduce the computational complexity, an early detection method and a reduced-search MAP algorithm were proposed. However, it was found that the variable computation effort used in the reduced-search MAP algorithm could introduce difficulties in the hardware implementation. Turbo codes are most effective dealing with longer code-words and less effective dealing with shorter code-words, in contrast to convolutional codes, whose performance is not affected with the code-word length. Moreover, turbo codes tend to consistently approach Shannon’s theoretical limit. They have very good performance with very low error probabilities down to 10−5 . However, randomly constructed turbo codes cause low-weight code-words, which in turn causes error floor that starts at that level, too.

3.2.8

Polar Code

Polar codes are linear block error correcting codes introduced by Erdal Arikan in 2009 [64]. They achieve the Shannon’s symmetric capacity of all binary-input memoryless channels. They are practical low complexity codes with time and space complexities of O(N log N ), where N is the codeword length. The code construction is based on recursive concatenation of a short kernel code which transforms a physical channel into virtual

3

Error Correction Techniques

91

channels. As the number of recursions increases, the virtual channels tend to polarize, creating extreme channels—either noiseless reliable channels or useless channels. At this point, the fraction of noiseless channels tends to the maximum possible fraction of the allowable transmitted data in the channel before the transformation, so the data bits are assigned to the noiseless channels while the rest of the channels are fixed with a value (frozen bits) that are predetermined to both the sender and the receiver [65, 66]. Consider a setup where (U1 , U2 ) are two equiprobable bits that are encoded into (X 1 , X 2 ) = (U1 ⊕ U2 , U2 ). Then, (X 1 , X 2 ) are mapped to (Y1 , Y2 ) by two independent BMS channels with transition probabilities [67]: P(Y1 = y|X 1 = x) = P(Y2 = y|X 2 = x) = W (y|x)

(11)

The mapping from (U1 , U2 ) to (X 1 , X 2 ) is invertible, and I (U1 , U2 ; Y1 , Y2 ) = I (X 1 , X 2 ; Y1 , Y2 ) = I (X 1 ; Y1 ) + I (X 2 ; Y2 ) = 2I (W )

(12)

where C = I (X 1 ; Y1 ) = I (W ) is the capacity of symmetric BMS channel because X 1 is equiprobable and     1 1 I (W )  W (y|0) log2 W (y|0)/ W (y|0) + W (y|1) (13) 2 2 y Thus, the transformation (X 1 , X 2 ) = (U1 ⊕ U2 , U2 ) preserves the sum capacity of the system. Also, the chain rule decomposition. I (U1 , U2 ; Y1 , Y2 ) = I (U1 ; Y1 , Y2 ) + I (U2 ; Y1 , Y2 |U1 ) = 2I (W )

(14)

Therefore, the capacity C(W1 ) + C(W2 ) = 2C(W ) = 2I (W ) is conserved. However, the capacity is redistributed unevenly between the channels, where C(W1 ) ≤ C(W ) ≤ C(W2 ), with equality if C(W ) equals 0 or 1. The rate 2I (W ) can be achieved by sending information through the virtual channel W1 : U1 → (Y1 , Y2 ) at a rate I (U1 ; Y1 , Y2 ) and decoded to Uˆ 1 , then the information is sent through W2 : U2 → (Y1 , Y2 , U1 ) at a rate I (U2 ; Y1 , Y2 |U1 ) and decoded to Uˆ 2 based on previous information Uˆ 1 . Repeated transformations cause the virtual channel to polarize into extremal channels whose capacities approach 0 or 1 [68]. As compared with Turbo codes and LDPC codes, Polar codes provides superior decoding performance in terms of bit error rate (BER) which is very close to the Shannon limit. Successive cancellation (SC) decoding algorithm is the most famous decoding algorithm for polar codes. The original decoder is the Successive cancellation (SC) proposed by Arikan [35]. The SC decoder has an architecture similar to the fast Fourier transform (FFT). The SC decoding algorithm sequentially estimates the information bits based on the received channel log-likelihood ratios (LLRs) and the encoding structure. Other

92

Wireless Communication Systems: Reliability

than the SC decoding algorithm, the belief-propagation (BP) algorithm can also decode polar codes by passing LLR-values based on the encoding graph in an iterative fashion. Compared with the SC decoding algorithm, BP decoding provides higher throughput and shorter latency, but suffers from a substantial error-rate performance degradation. For both SC decoding and BP decoding, research is working towards a balance between error-rate performance and complexity. Since some of the underlying mechanisms are difficult to model, artificial intelligent (AI) has been recently considered to optimize this tradeoff [69, 70].

3.2.9

CRC for Error Correction

This algorithm is based on the fact that “each possible single-error position in a packet gives a unique remainder after polynomial division of the polynomial corresponding to the packet by the generator polynomial, regardless of the actual bits transmitted in the packet”. As a result, a lookup table of all possible remainders and the corresponding error bit positions can be calculated in advance, using packets with one in error positions and zeros elsewhere. When an erroneous packet with a single bit error arrives, the corresponding remainder is calculated and checked against the lookup table, thus the error position is identified. This method can correct 100% of all single-error packets [69, 71].

4

Channel Equalization

In wireless communications, the receiver systems have to compensate the channel effects including Rayleigh fading, Doppler frequency shift, and additive white Gaussian noise (AWGN). Therefore, channel equalizer and forward error correction (FEC) techniques are exploited to improve the system performance. The channel equalizer is used to detect the original signals from the received signals with multipath effect. In an ideal communication channel, the received information is identical to that transmitted. However, this is not the case for real communication channels, where signal distortions take place. A channel can interfere with the transmitted data through three types of distorting effects: power degradation and fades, multi-path time dispersions and background thermal noise. Equalization is the process of recovering the data sequence from the corrupted channel samples. Channel equalization is employed to deal with the distortions introduced by effects caused by channels and system impairments. These effects contain inter-symbol interference (ISI) and nonlinearities caused by amplifiers as well as data converters. Optimum equalization, i.e., maximum-likelihood sequence estimation (MLSE) based on Viterbi algorithm (VA) is a promising technique used in GSM with binary Gaussian minimum-shift keying (GMSK) modulation scheme. Figure 13 shows the concept of channel equalization, where equalization can reduce the ISI and noise effects for better demodulation. There are two modes of operations for equalizers: training (channel estimation) and tracking. Training sequence is a known fixed bit pattern sent by the transmitter. The user data is sent immediately

4

Channel Equalization

93

Fig. 13 a Channel response, b equalized channel response

after the training sequence. The equalizer uses training sequence to adjust its frequency response and is optimally ready for data sequence.

4.1

Linear Equalization

A linear equalizer is a filter that can undo the channel effect. Ideally, the output of an equalizer is a delayed version of the transmitted signal. A fixed equalizer measures the time-invariant channel and compensates the frequency selectivity during the entire transmission of data. An adaptive equalizer adjusts its coefficients to track a slowly time-varying channel [72].

4.2

Adaptive Equalization

Equalization is the process of compensation at the receiver, to reduce noise effects. The channel is treated as a filter with transfer function. Equalization is the process of creating a filter with an inverse transfer function of the channel. Since the channel is a varying filter, equalizer filter also has to change accordingly, hence the term adaptive. Least Mean Square (LMS), Recursive Least squares (RLS) and Particle swarm optimization algorithms (PSO) are used to implement the adaptive channel equalizer as depicted in Fig. 14 [73].

94

Wireless Communication Systems: Reliability

Fig. 14 A basic linear equalizer during training: channel estimation

It consists of N delay elements, N + 1 taps, and N + 1 tunable complex weights updated continuously by an adaptive algorithm controlled by the error signal ek .

5

Diversity

Unlike equalization, diversity requires no training overhead since a training sequence is not required by the transmitter. It is a method that is used to develop the information from several input signals transmitted over the independent fading path. It Send same bits over independent fading paths then combine paths to mitigate fading effects. The receiver selects antenna with stronger signal (Fig. 15) [74].

Fig. 15 Diversity concept

6

Conclusions

5.1

95

Space Diversity or Antenna Diversity

It uses M antennas to receive M copies of the transmitted signal. The signals received from spatially separated antennas would have uncorrelated envelopes for antenna separations of one half wavelength or more.

5.2

Time Diversity

It transmits information at time spacing that exceed coherence time of the channel. It spread data out over time.

5.3

Code Diversity

It spread data out over code such as CDMA.

5.4

Polarization Diversity

Polarization Diversity require two transmitter and two receiving antennas with different polarization (horizontal and vertical). The transmission wave with two different polarization constitute two different paths. This provides only two different diversity branches. Polarization Diversity uses half power by dividing the power between two different polarized antennas.

5.5

Frequency Diversity

Frequency diversity transmits information on more than one carrier frequency such as OFDM modulation.

6

Conclusions

This chapter covers the management of errors in the received data stream by means of forward error correction and re-transmission. It introduces the channel codes such as the LDPC, the polar and the turbo codes. Moreover, it presents the corresponding encoding and decoding block for each channel code. ECCs improve reliability by encoding data in a redundant format that uses additional bits of information to identify, and correct, data bits that change value while in storage or transit.

96

Wireless Communication Systems: Reliability

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Wireless Communication Systems: Line Coding, Modulation, Multiple Access, and Duplexing

This chapter introduces a review of the different modulation, multiple access, duplexing, and line coding techniques and the various methods and tools that are used to implement it. Digital demodulations can be classified into coherent and non-coherent. In wireless communication, it is required to have a mechanism that provides communications services to multiple users at the same time. Throughout the years, there have been several multiple access schemes being used. Multiple Access is the use of multiplexing techniques to provide communication service to multiple users over a single channel. Spread spectrum is a modulation that increases signal bandwidth. It spreads modulated signal over wider bandwidth than needed for transmission. This makes it hard to track and jamming. Moreover, it enables multi-users.

1

Why Modulation?

Digital information is conveyed using various digital modulation techniques. Coupling EM wave into space needs antenna size ~ wavelength. For speech signal f = 3 kHz = > lamda (λ) = 105 m which means that we need an Antenna size = 105 m which is practically unrealizable. Hence, efficient antenna of realistic physical size is needed for radio communication system. That is why we need to modulate the original signal with another higher frequency signal [1, 2].

2

Types of Modulation

Figure 1 summarize the classification of modulations. Table 1 shows different types of modulations as a function of information/carrier signal type [17–24]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 K. S. Mohamed, Wireless Communications Systems Architecture, Synthesis Lectures on Engineering, Science, and Technology, https://doi.org/10.1007/978-3-031-19297-5_5

101

102

Wireless Communication Systems: Line Coding …

Modulation

Secondary

Primary

Pulse amplitude

Pluse width

Spread Spectrum

Carrier

Pulse

Pluse position

Analog

AM, FM

Digital

FHSS, DSSS

Constant envelope

Nonconstant envelope

PSK,FSK

ASK,QAM

Fig. 1 Classification of modulation format

Table 1 Types of modulation Type of information Type of carrier signal Modulation type

Examples

Analog

Analog

Carrier modulation

AM, FM

Analog

Digital

Pulse modulation

PAM, PWM, PPM

Digital

Analog

Single/multi Carrier modulation

ASK, FSK, PSK, QPSK, OFDM

Digital

Digital

Encoding Manchester encoder modulation/Line coding

2.1

Types of Pulse Modulation: Analog to Digital Up-Conversion

A. Pulse Amplitude Modulation (PAM) It is a modulation technique in which the amplitude of the pulsed carrier signal is changed according to the amplitude of the message signal. The amplitude of the pulses is varying with respect to the amplitude of analog modulating signal, like in case of amplitude modulation. But the major difference is that unlike AM, here the carrier wave is a pulse train rather than continuous wave signal (Fig. 2).

2 Types of Modulation

103

Fig. 2 PAM

B. Pulse Width Modulation (PWM) In PWM, the width of the pulses is varied according to the amplitude of the message signal. Unlike PAM, the amplitude of the signal is constant and only the width is varying. PWM technique is similar to frequency modulation because, by the variation in the width of the pulses, the frequency of the pulses in the PWM signal shows variation (Fig. 3) [3]. Fig. 3 PWM

104

Wireless Communication Systems: Line Coding …

Fig. 4 PPM

C. Pulse Position Modulation (PPM) It is a technique in which the position of the pulses is changed in accordance with the amplitude of the modulating signal. Here the pulse amplitude and the pulse width are the two constant that does not show variation with the amplitude of the modulating signal but only the position shows variation. It is to be noted here that the position of the pulse changes according to the reference pulses. And these reference pulses are nothing but PWM pulses. Basically, the falling edge of PWM pulses acts as the starting of the PPM pulses (Fig. 4) [4, 5].

2.2

Types of Analog Modulation: Analog to Analog Up-Conversion

In analog modulation formats, some parameter of the transmitted signal varies as a linear function of the amplitude of the original audio or video signal to be transmitted. Analog modulation schemes include amplitude, frequency, and phase modulation. A. AM In amplitude modulation (AM), information is impressed on a carrier wave by varying the amplitude of the carrier to match the fluctuations in the audio or video signal being transmitted. AM is the oldest method of broadcasting radio programs (Fig. 5).

2 Types of Modulation

105

Fig. 5 AM modulation

B. FM In frequency modulation (FM), unlike AM, the amplitude of the carrier is kept constant, but its frequency is altered in accordance with variations in the audio signal being sent (Fig. 6). A. PM The phase of a carrier wave is varied in response to the vibrations of the sound source in phase modulation (PM). This form of modulation is often considered a variation of FM. The two processes are closely related because phase cannot be changed without also varying frequency, and vice versa. Also, the rate at which the phase of a carrier changes is directly proportional to the frequency of the audio signal (Fig. 7).

2.3

Types of Encoding Modulation/Line Coding: Digital to Digital Up-Conversion

A. Manchester Encoding Digital data can include long, uninterrupted sequences of ones or zeros, and thus a standard digital signal used to transmit this data will remain at the same voltage for a

106

Fig. 6 FM modulation

Fig. 7 PM modulation

Wireless Communication Systems: Line Coding …

2 Types of Modulation

107

Fig. 8 Manchester encoding

relatively long period of time. Manchester encoding is a simple digital modulation scheme that ensures that the signal never remains at logic low or logic high for an extended period of time (Fig. 8). B. RZ Encoding Unipolar Return Zero (RZ) encoding ensures that the signal never remains at logic low or logic high for an extended period of time (Fig. 9). C. 8b/10b encoding In telecommunications, 8b/10b is a line code that maps 8-bit words to 10-bit symbols to achieve DC-balance and bounded disparity, and yet provide enough state changes to allow reasonable clock recovery. This means that the difference between the counts of ones and zeros in a string of at least 20 bits is no more than two, and that there are not more than five ones or zeros in a row. This helps to reduce the demand for the lower bandwidth limit of the channel necessary to transfer the signal. We need to prevent fixed bit stream repetition which may cause electro-magnetic interference (EMI). Every 8-Bit input data can be encoded to 10-Bit data by two possibilities first is positive disparity and second is a negative disparity (an example is shown in Table 2). The coding scheme breaks the original 8-bit data into two blocks, 3 most significant bits (y) and 5 least significant bits (x). From the most significant bit to the least significant bit, they are named as H, G, F and E, D, C, B, A. The 3-bit block is encoded into 4 bits named j, h, g, f. The 5-bit block

108

Wireless Communication Systems: Line Coding …

Fig. 9 Unipolar RZ encoding

is encoded into 6 bits named i, e, d, c, b, a. As seen in Fig. 11, the 4-bit and 6-bit blocks are then combined into a 10-bit encoded value. The disparity is required to employ a balanced number of 0 s and number of 1 s. The disparity of Block = number of 1 s—the number of 0 s. The block is Disparity Neutral if its disparity = 0. It works as follows Fig. 10: • Transmitter assumes negative disparity (RD-) at startup. • Then it works according the disparity FSM shown in Fig. 10.

Table 2 8b/10b Encoding options Data (0) or Control (1)

8 Bit input data

Dx,y/Kx,y

10-Bit output data (RD−)

10-Bit output data (RD+)

Disparity (RD−)

Disparity (RD+)

0

8’b_000_01110

D14.0

10’b_011100_1011

10’b_011100_0100

2

−2

0

8’b_010_01100

D12.2

10’b_001101_0101

10’b_001101_0101

0

0

0

8’b_000_01001

D9.4

10’b_100101_1101

10’b_100101_0110

2

−2

1

8’b_101_11100

K28.5

10’b_001111_1010

10’b_110000_0111

2

−2

1

8’b_111_11011

K27.7

10’b_110110_1000

10’b_001001_0111

0

0

1

8’b_111_11110

K30.7

10’b_011110_1000

10’b_100001_0111

0

0

2 Types of Modulation

109

Fig. 10 8b/10b Encoding

Fig. 11 Disparity FSM

2.4

Types of Digital Modulation: Digital to Analog Up-Conversion

In digital modulation formats, the signal to be transmitted is binary data that may or may not represent an analog signal that has been quantized and digitized. At any given time, the transmitter sends one of a discrete set of symbols, each of which represents one or more bits. Digital modulations can be classified as constant and non-constant envelope modulation (Table 3).

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Wireless Communication Systems: Line Coding …

Table 3 Types of digital modulation Non-constant envelope

Constant envelope

QAM

PSK-FSK

Spectral efficiency

Power efficiency

Amplitude is variable for all symbols so not power efficiency

Amplitude is constant for all symbols

2.4.1

Basics: Digital to Analog Up-Conversion

A. IQ Data IQ data encodes changes to the phase and amplitude of the carrier i.e. it is a simple way of representing amplitude and phase information. Assume the transmitted signal can be modeled by Eq. (1) S(t) = A cos(ωt + φ)

(1)

where A ω φ

is the amplitude, is the angular frequency, is the phase.

Let us represent Eq. (1) in another format using Eq. (2) which results in Eq. (3) cos(α + β) = cos(α) cos(β) − sin(α) sin(β)

(2)

S(t) = A cos(ωt + φ) = A cos(φ) cos(ωt) − A sin(φ) sin(ωt) = I cos(ωt) − Qsin(ωt)

(3)

where I Q

is the amplitude of the “in-phase carrier”, is the amplitude of the “quadrature phase carrier”. This can be described in Fig. 12. The IQ modulators can be shown in Fig. 13. IQ data can be used for any modulation scheme.

2 Types of Modulation

111

Fig. 12 IQ representation

Fig. 13 IQ modulator

B. Symbols For a digital communication, the information we wish to send from the transmitter to the receiver are the bits. Bits of information can represent anything from ASCII characters in a Microsoft Word document, to numeric values that represent samples from an audio signal, to numeric values that represent the colors of pixels in a digital image. The information is carried in the bits that are transmitted, but we don’t actually transmit bits; we transmit waveforms that represent bits. These waveforms are commonly referred to as symbols. Symbols are the physical means by which bits move from transmitter to receiver, and exactly how it is done depends on the communication medium being used. If we wish to send bits over a wire, we usually use voltage pulses. For example, a high pulse may represent a 1-bit and a low pulse (or no pulse) may represent a 0-bit (or vice versa). In this case, the voltage pulses are the symbols, and each pulse carries 1 bit of information. Using voltage pulses, the transmitter is sending one of two possible symbols (e.g. a high pulse or a low pulse), and the process of sending digital information with voltage pulses forms a baseband (low frequency) signal. C. Constellation Diagram Constellation diagram is graphical way or a plot of the phase and relative amplitude of the output symbols for a digital modulation system, in polar coordinates. An example is shown in Fig. 14.

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Wireless Communication Systems: Line Coding …

Fig. 14 Constellation diagram of PSK

2.4.2

Modulation Techniques: Digital to Analog Up-Conversion

D. Amplitude Shift Keying (ASK) In ASK modulation (Fig. 15), the transmission of a 1 bit or a 0 bit is represented by turning the signal on or off. As with AM, the ASK modulates the amplitude of the carrier depending on the value of information. Below, we see a figure depicting the ASK modulation. E. Frequency Shift Keying (FSK) In FSK modulation (Fig. 16), the transmission of either bit is represented by a difference in frequency from the original carrier wave. In this case, a zero is represented by a frequency less than the original carrier frequency and a one is represented by a frequency more than the original carrier frequency. This is similar to the analog FM. Below, we see a figure depicting the FSK modulation. F. Phase Shift Keying (PSK) In PSK modulation (Fig. 17), the transmission of either bit is represented by a difference in phase from the original carrier wave. A one is represented by the original carrier Fig. 15 ASK example

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113

Fig. 16 FSK example

Fig. 17 PSK example

signal (no phase difference) and the zero is represented by a 180 degree phase difference (negative amplitude). Below, we see a figure depicting the PSK modulation. G. M-Ary Modulation Multi-level modulation techniques permit high data rates within fixed bandwidth constraints. High-order modulation can increase the spectral efficiency of communication but using high modulation order directly increases the bit error rate due to increasing the constellated symbol interference. (1) M-ary Quadrature Amplitude Modulation (QAM) An M-ary transmission is a type of digital modulation where instead of transmitting one bit at a time, two or more bits are transmitted simultaneously. This type of transmission results in reduced channel bandwidth. However, sometimes, two or more quadrature carriers are used for modulation. This process is known as quadrature modulation. 16-QAM has 4 I values and 4 Q values yielding 4 bits per symbol. It has 16 states or constellations because 24 = 16. The constellation diagram of 16-QAM is presented in Fig. 18. Table 4 shows transmitted symbols with corresponding carrier phase and amplitude. There are higher level of QAM such as 64-QAM (Fig. 20) and 256-QAM (Fig. 21). Block diagram of 16-QAM modulation is shown in Fig. 22 (Fig. 19). And, the transmitted bits will be assigned to constellations as follows (Fig. 19):

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Wireless Communication Systems: Line Coding …

Fig. 18 Constellation diagram of 16-QAM

Table 4 QAM transmitted symbols with carrier phases and amplitude Symbol transmitted

Carrier phase (°)

Carrier amplitude

0000

225

0.33

0001

255

0.75

0010

195

0.75

0011

225

1.0

0100

135

0.33

0101

105

0.75

0110

165

0.75

0111

135

1.0

1000

315

0.33

1001

285

0.75

1010

345

0.75

1011

315

1.0

1100

45

0.33

1101

75

0.75

1110

15

0.75

1111

145

1.0

2 Types of Modulation Fig. 19 Constellation diagram of 16-QAM

Fig. 20 Constellation diagram of 64-QAM

Fig. 21 Constellation diagram of 264-QAM

115

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Wireless Communication Systems: Line Coding …

Fig. 22 Block diagram of 16-QAM modulation

(2) M-ary PSK In MPSK, the phase of the carrier takes on one of M possible values = 1, 2,…, M. The MPSK signal set is thus analytically given by: ) ( 2πi − 1 S(t) = A cos ωt + M

2π i−1 M ,

where i

(4)

We can further increase the number of bits per symbol by increasing the number of possible phase shifts. The constellation diagram of 8-PSK is shown in Fig. 23. H. Differential Modulation Essentially, differential encoding (which includes DBPSK) is a technique that makes data to be transmitted to depend on both the current bit as well as the previous bit received. It uses the data to change the phase rather than set the phase. Fig. 23 Constellation diagram of 8-PSK

2 Types of Modulation

117

Fig. 24 Differential encoding

To illustrate the usefulness of differential encoding, an example is done below on a data stream of bits. To compare to the phase shift encoding needed for DBPSK, imagine that the first bit determines your initial phase shift and the subsequent bits are the next shifts. Encoding is determined by initial reference bit (either 0 or 1) that the circuit or software chooses. The incoming data sequence is added (binary math) to this reference bit and forms the second bit of the encoded sequence. This bit is then added to the next data bit to continue the process as shown below. Note that the carry bit from the binary addition is discarded, as it is not necessary in the encoding sequence (Fig. 24). For DBPSK we can say that the initial bit 1 (or state) represents an initial phase shift of 180 degrees. Since the next number is 1, there is another 180 phase shift and the resulting encoded output is 0 (goes all the way back around to state 0). The same is done for the next bit. Since the zero represents a zero degree phase shift, the next encoded state is also zero. Again, since the next bit is a 180 degree phase shift, the next encoded state is now 1. This continues until all the data is encoded. We can see DBPSK a little better below with a different data stream and an actual phase shifted signal (Fig. 25). We can still see that encoding scheme for the graph is the same as the encoding scheme example above. The first ‘1’ is now represented by a 180 degree difference from the initial phase (which we do not see, the initial bit is cut off). The next ‘1’ shifts the signal again by 180 degrees. The next three ‘0’ data bits keep the signal in the same phase. The next two ‘1’ data bits change the phase back and forth. The final ‘0’ bit keeps the signal the same. The decoding process reverses the above. The incoming bits are added together to recreate the input data sequence. We will explore the possibility that the signal has in fact inverted and thus highlight why DBPSK is used. Below we see both the normal and inverted possibilities (Fig. 26).

Fig. 25 Differential encoding for DBPSK

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Wireless Communication Systems: Line Coding …

Fig. 26 Immunity of differential encoding against polarity reversal

Fig. 27 Differential encoding

Even if there was signal inversion, as you can see, the decoded data sequence is the same, and thus protects against any inversion errors. As with the previous example, we can apply the same principles to a DBPSK signal (Fig. 27). Using the signal above, we can derive an encoded sequence based on the DBPSK signal. This encoded sequence is dependent on the phase shifts. So assuming an initial phase shift of 0°, we derive the following encoded sequence. Encoded sequence:

0

1

0

0

0

0

1

0

0

As you can tell, the first phase shift to 180 degrees from the initial 0 degree phase shift results in the second number in being 1. Since there is another phase shift of 180 degrees, the third number is 0 since it returns to its initial phase. Three consecutive 0 s result in no shift changes, or three encoded zeros. The next 1 changes the phase to 180° and thus encoded a 1. Another 1 changes the phase back to 0 and thus encodes a 0. The last 0 does not change anything and thus encodes a 0. Now using the encoded series, we can decoded and obtain our data by adding each number. Encoded sequence: Decoded (Data):

0

1

0

0

0

0

1

0

0

1

1

0

0

0

1

1

0

2 Types of Modulation

119

Fig. 28 Constellation diagram of QPSK

The decoded data matches our initial data sequence; therefore we have successfully transferred data through DBPSK. I. QPSK In QPSK, “Quadrature” means the signal shifts among phase states that are separated by 90°. The signal shifts in increments of 90° from 45° to 135°, − 45° (315°), or − 135° (225°). Data into the modulator is separated into two channels called I and Q. Two bits are transmitted simultaneously, one per channel, thus doubling the spectral efficiency compared to BPSK. Constellation diagram of QPSK is given in Fig. 28. J. Offset: π/4QPSK π/4-DQPSK has no 180° phase shifts as the maximum phase transition is limited to 135° thereby limiting spectral growth, This allows us to place channels closer to each other thereby maximizing the limited spectrum of the ISM band. In addition, it can be differentially demodulated. These properties make it particularly suitable for mobile communication, where differential modulation can reduce the adversary effect of the fading channel.

2.4.3

Comparison Between Different Modulation Schemes: Digital to Analog Up-Conversion

A comparison between different modulation schemes can be shown in Table 5. In PSK the transmitter can operate at the maximum power, but in QAM it is not possible to use the maximum power to send the symbols that their amplitude are less than the maximum power.

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Wireless Communication Systems: Line Coding …

Table 5 Modulation type comparison Modulation type

Advantage

Disadvantage

ASK

Better BER

Low BW efficiency

PSK

Power efficiency

LOWER BER

FSK

FSK is a constant envelope modulation hence insensitive to amplitude variations in the channel hence compatible with non-linear transmitter and receiver systems

• FSK is slightly less bandwidth efficient than ASK or PSK • The bit/symbol error rate performance of FSK is worse than PSK

M-ary/QAM

Bandwidth Efficiency

• Reduced immunity to noise • As a general rule, we know that as the number of symbol states is increased, the tolerance to noise is reduced • Increased transmission power compared to binary

Differential

Provide polarity reversal protection

• DPSK performs less than BPSK because the errors tend to propagate due to correlation between bit waveforms

QPSK

• High side-lobes in the power spectrum requires filtering • Filtering introduces envelope fluctuation • Envelope fluctuation causes spectrum spread in HPA operation • Saturated HPA operation restores removed sidelobes • Restoration of sidelobes forces HPA operation below saturation (5 dB output and 10 dB input backoff) • Contradiction to power efficient modem

Offset/π/4QPSK

2.5

• Reduce envelope fluctuation as phase changes of 180 is prohibited • Ensure that the modulation envelope of the QPSK signal never passes through zero

Complex

Adaptive Modulation

If the effect of noise and interference is large enough, it may result in bit errors (Fig. 29). The error rate depends on two factors: the signal to noise ratio (SNR) at the receiver, and the choice of modulation scheme. In a fast modulation scheme such as 256-QAM,

2 Types of Modulation

121

Fig. 29 a BPSK signal, b noisy BPSK signal

signal is vulnerable to errors and can only be used if the SNR is high. In contrast, QPSK only has a few states, so it is less vulnerable to errors and can be successfully used at a lower SNR. Some communication systems exploits use what is called adaptive modulation which means switching dynamically between different modulation techniques according to the noise and interference level.

2.6

Types of Digital Demodulation

Digital demodulations can be classified into coherent and non-coherent. Table 6 summarizes the differences between them. Coherent demodulation is better for slow fading which is caused by large obstructions between transmitter and receiver and for AWGN channel whose voltage distribution over time has characteristics which can be described using a Gaussian, normal or statistical distribution. i.e., bell curve. The voltage distribution has zero mean and standard deviation. While Non-coherent demodulation is better for fast fading channels which is caused due to scattering of the signal by an object near the transmitter.

2.7

Factors Affecting Choice of Modulation

There are many factors that affects choosing a certain modulation techniques. These factors are summarized in Table 7.

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Wireless Communication Systems: Line Coding …

Table 6 Types of digital demodulations Coherent

Non-coherent

Phase information

Needed

Not needed

Better performance

AWGN channel slow fading channel

Fast fading channel

Table 7 Factors affects choice of modulation Metric

Definition

Modulation example

Communication example

Signal-to-noise ratio (SNR)

SNR

All

All

Probability of error or Bit error rate (BER)

BER

All

All

Power efficiency

A measure of how much received power is needed to achieve a specified BER (inversely proportional to BER)

PSK

Satellite communication

Bandwidth efficiency or The ratio of the bit rate to M-ary spectral efficiency the channel bandwidth

2.8

Microwave radio

Multiple Access and Spread Spectrum

In wireless communication, it is required to have a mechanism that provides communications services to multiple users at the same time. Throughout the years, there have been several multiple access schemes being used. Multiple Access is the use of multiplexing techniques to provide communication service to multiple users over a single channel. It allows for many users at one time by sharing a finite amount of spectrum. Multiple Access Option can be: Frequency (FDMA), Time (TDMA), Code (CDMA), and Space (SDMA) as depicted in Fig. 30. Changes of Codes with time and frequency is shown in Fig. 31.

2.8.1

FDMA

The available bandwidth is subdivided into a number of narrower band channels. Each user is allocated a unique frequency band in which to transmit and receive on. Only one user per radio channel (Fig. 32). Several FSK signals can be transmitted simultaneously in a given frequency band by assigning different center frequencies to each of the FSK signals. This method of simultaneous transmission is called FDM.

2 Types of Modulation

Fig. 30 Multiple access schemes

Fig. 31 Multiple access schemes (frequency, time, code)

Fig. 32 FDMA

123

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Wireless Communication Systems: Line Coding …

Fig. 33 TDMA

Fig. 34 FHSS concept

2.8.2

TDMA

Frames are used to allow the communication receiver to be able to determine where each users data is locate within the bit stream it receives. GSM uses a combination of both TDMA and FDMA techniques. The uplink band in GSM has a total of 25 MHz of bandwidth and each radio channel has an assigned bandwidth of 200 kHz. The number of radio channels (FDMA) = 125 channels, actually it uses only 124 channels. Each channel is divided into 8 time slots, so 8 users are allowed per radio channel (TDMA). The maximum number of users is then 992 users (Fig. 33).

2.8.3

Spread Spectrum Techniques

Spread spectrum is a modulation that increases signal bandwidth. It spreads modulated signal over wider bandwidth than needed for transmission. This makes it hard to track and jamming. Moreover, it enables multi-users.

FHSS It is a Pseudo-random frequency hopping (Fig. 34), so it becomes hard to jam. PSK (phase shift keying) and FSK (frequency shift keying) are two common modulation techniques to implement spread spectrum. PSK uses a phase shift of ± for a chip change. Such a modulated signal is termed Direct Sequence Spread Spectrum signal. If the code is used to modulate an M-ary FSK then it results in Frequency Hopping Spread Spectrum (FHSS). SS reduces EMI. The basic FH modulation system comprises a digital phase or frequency shift keying modulator and a frequency synthesizer. The latter generates carrier frequencies according to the pseudo-random phases of the spreading code sequence that is then mixed with the data carrier to originate the FH signal. In the basic FH receiver, the received FH signal is first filtered using a wideband bandpass filter and then mixed with a replica of the FH carrier. The mixer output is

2 Types of Modulation

125

Fig. 35 a FHSS transmitter, b FHSS receiver

applied to the appropriate demodulator. A coherent demodulator may be used when a PSK carrier is received (Fig. 35) [6, 7]. It is used in Bluetooth.

CMDA FHSS is used to avoid interference, to prevent eavesdropping, and to enable code-division multiple access (CDMA) communications. In CDMA, n users each using different orthogonal PN sequence, we modulate each user’s data stream using BPSK then multiply by spreading code of user. In CDMA, every channel uses the full available spectrum. Individual conversations are encoded with a pseudo- random digital sequence and then transmitted. There are 64 Walsh codes available to differentiate between calls. In fact, many different “signals” baseband with different spreading codes can be modulated on the same carrier to allow many different users to be supported. Using different orthogonal

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Wireless Communication Systems: Line Coding …

Fig. 36 CDMA concept

Information bits Code at transmitting end

Transmitted signal

Received signal Code at receiving end Decoded signal at the receiver

Fig. 37 CDMA example

codes, interference between the signals is minimal. Conversely, when signals are received from several mobile stations, the base station is capable of isolating each because they have different orthogonal spreading codes. CDMA uses two important types of codes to channelize users. Walsh codes channelize users on the forward link (BTS to mobile). Pseudorandom Noise (PN) codes channelize users on the reverse link (mobile to BTS). In CDMA, Instead of modulating with a sinusoid, we will modulate the sequence #1 with this new binary sequence which we will call the code sequence for sequence #1 (Fig. 36). An example is shown in Fig. 37.

DSSS Direct-sequence spread spectrum (DSSS) is a spread-spectrum modulation technique primarily used to reduce overall signal interference. The direct-sequence modulation makes the transmitted signal wider in bandwidth than the information bandwidth (Fig. 38).

2 Types of Modulation

127

Fig. 38 Signal spectrum a before DSSS, b after DSSS

After the de-spreading or removal of the direct-sequence modulation in the receiver, the information bandwidth is restored, while the unintentional and intentional interference is substantially reduced. With DSSS, the message bits are modulated by a pseudorandom bit sequence known as a spreading sequence. Each spreading-sequence bit, which is known as a chip, has a much shorter duration (larger bandwidth) than the original message bits. The modulation of the message bits scrambles and spreads the pieces of data, and thereby results in a bandwidth size nearly identical to that of the spreading sequence. The smaller the chip duration, the larger the bandwidth of the resulting DSSS signal; more bandwidth multiplexed to the message signal results in better resistance against interference. DSSS is used with some WLAN standards and GPS.

2.8.4

SDMA

Space-division multiple access (SDMA) is a channel access method based on creating parallel spatial pipes (focused signal beams) using advanced antenna technology with highly directional beams. SDMA can be depicted in Fig. 39 [8].

2.8.5

OFDMA

One of the limiting factors in the performance of mobile wireless communication systems is the Inter symbol interference (ISI), caused by the multipath. In single carrier systems the symbol duration (for large system capacity) is very small and spans a wide bandwidth in frequency domain and the multipath arriving at different time instants is spread over multiple symbols leading to ISI. The complex solution is to implement an equalizer at the receiver to mitigate the effect of the channel. A much simpler solution is to opt for multicarrier systems, like OFDM, which transmit low rate data (large symbol time) on several overlapping orthogonal subcarriers. In addition a guard time is provided (Figs. 40

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Wireless Communication Systems: Line Coding …

Fig. 39 SDMA

Fig. 40 OFDMA. It allows time and frequency DMA (2D scheduling)

and 41) at the start of each symbol. By doing so, the symbol time is made large enough so that the system becomes less sensitive to multipath. OFDMA solves the problem of (ISI) so it don’t need channel equalization and it is high bandwidth efficiency (Fig. 42). Moreover, it minimizes the effect of time-dispersion [9, 10]. OFDMA is a multi-user version of OFDM. OFDMA allows multiple users with varying bandwidth needs to be served simultaneously. OFDM combines the benefits of Quadrature Amplitude Modulation (QAM) and Frequency Division Multiplexing (FDM) to produce a high-data-rate communication system. QAM refers to a variety of specific modulation types: BPSK (Binary Phase Shift Keying), QPSK (Quadrature Phase Shift Keying), 16QAM (16-state QAM), 64QAM (64-state QAM). OFDM Tx and Rx is shown in Fig. 12. A key enabler for OFDM is the use of the Inverse Fast Fourier Transform (IFFT) to efficiently create the time domain waveform from the array of modulated subcarriers. The basic concept of OFDM recognizing that bandlimited orthogonal signals can be combined with significant overlap while avoiding inter-channel interference. Using OFDM, we can create an array of subcarriers that all work together to transmit information over a range of frequencies. These subcarriers must be orthogonal functions. Orthogonality between two functions is defined as the integral of their product over the designated

2 Types of Modulation

129

Fig. 41 OFDMA versus FDMA: OFDMA saves BW

Fig. 42 OFDM Tx and Rx

time interval is zero [11]. The FFT/IFFT which are the main component of OFDM are discussed below: • FFT: A fast Fourier transform (FFT) is an algorithm that computes the Discrete Fourier Transform (DTF) of a sequence, or inverse (IDFT) Fourier analysis converts a signal from its original domain (often time or space) to representation in the frequency domain and vice versa. • IFFT: is a quick calculation to reform opposite (or in reverse) Fourier change (DFT) which fixes the procedure of DFT. IDFT of an arrangement that can be characterized

130

Wireless Communication Systems: Line Coding …

Fig. 43 CSS

as: If an IFFT is performed on a complex FFT result registered by origin, this will on a basic level change the FFT result back to its unique informational collection.

2.8.6

Chirp Spread Spectrum (CSS)

Chirp spread spectrum (CSS) is a spread spectrum technique that uses wideband linear frequency modulated chirp pulses to encode information. A chirp is a sinusoidal signal whose frequency increases or decreases over time (often with a polynomial expression for the relationship between time and frequency). Chirp Spread Spectrum was developed for radar applications. If the frequency changes from lowest to highest, it is call up-chirp and if the frequency changes from highest to lowest, we call it down-chirp [12] (Fig. 43).

2.9

Duplexing

A duplex communication system is a point-to-point system composed of two or more connected parties or devices that can communicate with one another in both directions. Duplex systems are employed in many communications networks, either to allow for simultaneous communication in both directions between two connected parties or to provide a reverse path for the monitoring and remote adjustment of equipment in the field. There are two types of duplex communication systems: full-duplex (FDX) and half-duplex (HDX) [13].

2.9.1

Half-Duplex

In a half-duplex system, both parties can communicate with each other, but not simultaneously.

2.9.2

Full-Duplex

In a full-duplex system, both parties can communicate with each other simultaneously. Simultaneous transmission and reception on the same frequency can happen. TimeDivision Duplexing (TDD) is a duplexing technology that aims to use the same frequency

References

131

to provide continuous flow of information in both directions. TDMA, on the other hand, is a multiplexing technology. Its main goal is to combine multiple signals into a single channel. This is used in cellular applications where hundreds of cellphone units may be connecting to a single base station. Frequency-Division Duplexing (FDD) requires two separate communications channels. Wireless systems need two separate frequency bands or channels. In mobile communications, the FD paradigm allows uplink and downlink transmissions to occur simultaneously on the same frequency channel and has the potential of doubling the spectral efficiency of conventional half-duplex communication systems [14, 15].

2.10

Polarization Reuse

Frequencies can also be used to transmit and receive two simultaneous signals that have different polarization states. This can increase capacity by up to a factor of two. Ideally, orthogonal polarizations are used [16].

3

Conclusions

This chapter addresses the principles of digital signal processing for wireless communication such as line coding, modulation, multiple access, and duplexing.

References 1. J.G. Proakis, Digital Communications, 5th edn. (McGraw Hill, New York, 2002), pp. 1–16 2. B.P. Lathi, Signal Processing and Linear Systems (Oxford University, Oxford, 2000), pp.232– 287 3. D. Shiu, J.M. Kahn, Differential pulse-position modulation for power-efficient optical communication. IEEE Trans. Commun. 47(8) (1999) 4. H. Zhang, T.A. Gulliver, Pulse position amplitude modulation for time-hopping multiple-access UWB communications. IEEE Trans. Commun. 53(8) (2005) 5. K. S. Mohamed, FPGA implementation of PPM I-UWB baseband transceiver, in Proceedings of the European computing conference (Springer, Boston, MA, 2009) 6. K. Fazel, Performance of CDMA/OFDM for mobile communication system, in Proceedings of the IEEE International Conference on Universal Personal communications (1993), pp. 975–979 7. Internet Magazine. Spread Spectrum Scene: ABC’s of Spread Spectrum, http://sss-mag.com/ss. html 8. W.C.Y. Lee, Mobile Cellular Telecommunications: Analog and Digital Systems (McGraw-Hill, New York, 1995) 9. S.B. Weinstein, The history of orthogonal frequency-division multiplexing [history of communications]. Commun. Mag. IEEE 47, 26–35 (2009)

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10. Y. Zhang, J. Li, Y. Zakharov, X. Li, J. Li, Deep learning based underwater acoustic OFDM communications. Appl. Acoust. 154, 53–58 (2019) 11. Concepts of orthogonal frequency division multiplexing (OFDM) and 802.11 WLAN, Keysight technologies web page, http://rfmw.em.keysight.com/wireless/helpfiles/89600B/WebHelp/Sub systems/wlan-ofdm/Content/ofdm_basicprinciplesoverview.htm 12. J.G. Proakis, Digital Communications, 4th edn. (2001) 13. http://www.differencebetween.net/technology/communication-technology/difference-betweentdd-and-tdma/ 14. A. Sabharwal, P. Schniter, D. Guo, D.W. Bliss, S. Rangarajan, R. Wichman, In-band full-duplex wireless: Challenges and opportunities. IEEE J. Sel. Areas Commun. 32(9), 1637–1652 (2014) 15. C.B. Barneto, et al., Full-Duplex OFDM radar with LTE and 5G NR waveforms: challenges, solutions, and measurements. IEEE Trans. Microw. Theory Techn. 67(10), 4042–4054 (2019) 16. W.L. Stutzman, Polarization in Electromagnetic Systems (Artech House, Boston, MA, 1993) 17. S. Gupta, Wireless communication. IJSER 3(8), 1–8 (2012) 18. Y.G. Li, G.L. Stüber, Orthogonal Frequency Division Multiplexing for Wireless Communications (Springer Science + Business Media, Inc., U.S.A., 2006), p. 8 19. A.F. Molisch, Wireless Communications (Wiley, India, 2011), p. 399 20. T.S. Rappaport, Wireless Communications Principles and Practice (Pearson, India, 2010), pp. 322–323 21. A.D. Mahor, A. Kumar, Performance evolutionary in AWGN channel for 802.11a high speed network. IJARCSSE 2(2) (2012) 22. A. Gilat, MATLAB an Introduction with Applications (Wiley, India, 2010), p. 65 23. A. Kamboj, G. Kaushik, Study and simulation of O.F.D.M. system. IJMER 2(1), 235–241 (2012) 24. V. Sridhar, M.V. Bramhananda Reddy, M. Nagalaxmi, G. Nagendra, M. Renuka, BER and simulation of OFDM modulator and demodulator for wireless broadband applications. IJARCET 1(4), 201–209 (2012)

Wireless Communication Systems: Standards

During recent years, we have witnessed the explosive growth of wireless devices with heterogeneous technologies. In this chapter, we discuss some examples of different wireless communication systems standards such as Bluetooth, WiFi, LTE, DVB-S, and GPS. Moreover, a comparative study between all these standards are provided. Different communication systems classifications according to coverage range. Wireless communication includes a wide range of network types and sizes. The classification includes: Global network (GN): such as GPS and mobile satellite services, Broadcast network (BN): such as digital video/audio broadcasting and mobile TV, Wide area network (WAN): such as 4G/5G cellular, Local area network (LAN): such as WiFi, Personal area network (PAN): A PAN technology provides communication over a short distance such as Bluetooth, and Body area network (BAN): such as wireless wearables.

1

Wireless Body Area Network (WBAN)

The consumers and healthcare service providers using smart phones are growing exponentially throughout last decade (Fig. 1). Body area network which can be called also Body-Centric Computing are very promising. Wireless Body Area Network (WBAN) is a collection of low-power, miniaturized, invasive/non-invasive lightweight wireless sensor nodes that monitor the human body functions and the surrounding environment. Many sensors can be used such as ECG, movement, temperature and humidity. Electrocardiograph (ECG) is a device that records and measures the electrical activity of the heart over time. The frequency of measurement is a few times a day or continuously for periods of a few hours in some use cases. The IEEE 802.15.6 standard was designed for WBANs [1]. This Standard aims to provide an international standard for low power, short range, and extremely reliable wireless communication within the surrounding area of the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 K. S. Mohamed, Wireless Communications Systems Architecture, Synthesis Lectures on Engineering, Science, and Technology, https://doi.org/10.1007/978-3-031-19297-5_6

133

134

Wireless Communication Systems: Standards

Fig. 1 BAN example

human body, supporting a vast range of data rates for different applications. Short-range, wireless communications in the vicinity of, or inside, a human body (but not limited to humans) are specified in this standard. BANs has a wide range of applications, covering both medical to non-medical domains such as emotion detection, entertainment, and secure authentication applications [2, 3].

2

PAN: Bluetooth Transceiver

Wireless PANs play a significant role in short-range wireless communications, especially for Internet of Things (IoT) devices. Different wireless standards are available like IEEE 802.15.1 (Bluetooth), IEEE 802.11 (Wi-Fi), IEEE 802.15.4 for ZigBee, and many more. These wireless standards work at different frequency ranges and for different applications. Nowadays, these wireless standards are adopted in IoT environments for different applications like Home automation, Smart city, medical care, industrial control systems, and agriculture field. The wireless standards have different bandwidth requirements based on the applications. The larger bandwidth consumes more network constraints and more power utilization [4]. Bluetooth Low Energy (BLE) is a 1 Mbps data rate link that uses Gaussian pulseshaped frequency shift key with a modulation index of between 0.45 and 0.55. BLE channels are spaced equally across the 2.4 GHz ISM band (between 2.4 and 2.485 GHz) with 2 MHz channel spacing. A Bluetooth PAN is also called a piconet (very small network) that typically has a range of 10 m. Some applications of Bluetooth includes:

3

LAN: WiFi Transceiver

135

Source Encoder

Encrypt

Channel Encoder

Modulation

Spread Spectrum

Voice Codec

SAFER+ block cipher

Hamming code

GFSK

FHSS

RF

Fig. 2 The simplified Bluetooth transmitter. The receiver is the inverse operations of the transmitter

• Wireless communication between a mobile phone and a remote headset. • Wireless communication between a mobile phone and a Bluetooth car stereo system. • Wireless communication with PC input and output devices, like mouse, keyboard and printer. • Wireless transmission of audio, as a more reliable alternative to FM transmitters. The simplified Bluetooth transceiver is shown in Fig. 2.

3

LAN: WiFi Transceiver

Nowadays, more and more networks are operating without cables, within the wireless mode. Wireless LANs use high-frequency radio signals, infrared beams, or lasers to speak between the workstations, file servers, or hubs. Wireless LAN is formed by connecting different devices through wireless communication to form an area network. WLAN follows a typical standard named IEEE 802.11. WiFi stands for “Wireless Fidelity”, where Fidelity means compatibility between wireless equipment from different manufacturers. WiFi evaluation is described in Fig. 3. The simplified WiFi transmitter is shown in Fig. 4. The receiver is the inverse operations. WiFi is present in nearly all indoor environments. 802.11 is fundamentally based on a constellation of phase and amplitude modulation. WiFi network topology can be classified into [5, 6]: • Point-to-Multipoint (Access Point) • Point-to-Point (Ad hoc) • Multipoint-to-Multipoint (Mesh Network)

136

Wireless Communication Systems: Standards

Fig. 3 WiFi evolution

Source Encoder

Encrypt

Channel Encoder

Modulation

Spread Spectrum

?

AES

Hamming code

8 QPSK, 16-QAM, 64-QAM

DSSS

RF

Fig. 4 The simplified WiFi transmitter. The receiver is the inverse operations of the transmitter

4

WAN: 4G/LTE Transceiver

The introduction and increasing use of mobile devices (e.g., smart phones and tablets) and various mobile applications have dramatically changed the landscape of mobile communication systems and driven the development of cellular technologies from one generation to another. As the dominant 4G technology, LTE (Long Term Evolution) was developed by 3GPP starting in 2004 to meet the requirements of diverse mobile applications [7]. LTE is a release-based technology family. In every one to two years, a new release of various key features is specified to meet the requirements of emerging use cases, to support technologies from latest researches, and to address practical issues seen in real deployments. In the past decade, LTE family has grown to include LTE, LTE-Advanced, LTE-Advanced Pro and now its further evolution towards satisfying. 5G requirements and use cases. The key features of each major phase in LTE are shown in Fig. 5. LTE is the first and foundational release of LTE, where many fundamental features such as waveform, multiple access, modulation and coding schemes, and multi-antenna based transmission schemes are defined to meet the performance requirements. Further enhancements of LTE

5

BN: DVB-S Transceiver

137

Fig. 5 The key features of each major phase in LTE

Source Encoder

Encrypt

Channel Encoder

Modulation

Spread Spectrum

Robust Header Compression (ROHC)

Symmetrickey

Turbo code

QPSK, 64-QAM

OFDMA

RF

Fig. 6 The simplified LTE transmitter. The receiver is the inverse operations of the transmitter

were introduced since LTE-Advanced, including CA (carrier aggregation), CoMP (coordinated multi-point), more advanced MIMO (multiple-input multiple-output) technologies and relay. The introduction of LTE-Advanced significantly improved the capability of LTE in order to fulfill all the requirements of IMT-Advanced and to be qualified as a 4G technology. In 2015, 3GPP approved a new LTE marker, i.e., LTE-Advanced Pro. Furthermore, use cases and requirements of vertical services were taken into account in the new LTE release. The simplified LTE transceiver is shown in Fig. 6 [8]. In 5G, there is a set of modulations that the radio interface supports. For proper selection of the modulation scheme, a modulation mapper is applied. It receives binary values (0 or 1) as input and produces complex-valued modulation symbols as output. These output modulation symbols can be π/2-BPSK, BPSK, QPSK, 16-QAM, 64-QAM, and 256-QAM.

5

BN: DVB-S Transceiver

DVB (Digital Video Broadcasting) is a set of international open standards for digital television. There are many classification for DVB as follows [9]:

138

Wireless Communication Systems: Standards

Fig. 7 An illustration for DVB-S concept

Source Encoder

Encrypt

Channel Encoder

Modulation

Spread Spectrum

MPEG

CSA

Reed Solomon Code /LDPC coder

256-QAM

OFDMA

RF

Fig. 8 The simplified DVB-S transmitter. The receiver is the inverse operations of the transmitter

• DVB-T (Terrestrial). • DVB-S (Satellite). • DVB-C (Cable). DVB-S is a digital video broadcasting via Satellite (Fig. 7). The simplified DVB-S transceiver is shown in Fig. 8. The latest DVB standard, known as DVB-S2X (extension to DVB-S2) has introduced a set of innovations, such as high efficient APSK modulations and low roll-off factors, to improve the overall efficiency of satellite links [10–15].

6

GN: GPS Transceiver

GPS system is based on a ranging method, where the user position is calculated from measured propagation delays of the navigation signals between the navigation satellites and user receiver. The position velocity and time (PVT) determination algorithm also needs navigation satellite positions. Position is determined by the travel time of a signal from

7

A Comparative Study

139

Time Satellite tx signal

Receiver rx signal

Fig. 9 GPS delay calculation

four or more satellites to the receiving antenna. i.e. time difference between transmitted and received signal (Fig. 9). Three satellites for X, Y, Z position, one satellite to solve for clock biases in the receiver as positioning requires very precise time bases (Fig. 10). Assume, Signal leaves satellite at time t1 . Signal is picked up by the receiver at time t2 . So, distance between satellite and receiver (ρ) = (t2 −t1 ) * the speed of light as summarized in Eq. (1). Then, the position can be calculated by Eq. (2), where all variables are demonstrated in Fig. 11 [16]. The simplified GPS transceiver is shown in Fig. 12 [17, 18]. ρi = (t2 − t1 ) ∗ C

(1)

/ ρi =

(xi − x)2 + (yi − y)2 + (z i − z)2

(2)

From (1), (2), the only variables are (x, y, z) which are the required position.

7

A Comparative Study

Table 1 shows a comparative study between different wireless protocols such as Bluetooth, WiFi, LTE, DVB, GPS.

140

Wireless Communication Systems: Standards

Fig. 10 GPS concept

GPS receiver (x,y,z)

Sat3 (x3,y3,z3)

Fig. 11 GPS calculation

References

141

Source Encoder

Encrypt

Channel Encoder

Modulation

Spread Spectrum

-

-

LDPC Encoder

BPSK

CDMA

RF

Fig. 12 The simplified GPS transmitter. The receiver is the inverse operations of the transmitter

Table 1 A comparative study between different wireless protocols Standard

Family

Down-link

PAN

Bluetooth

Bluetooth

LAN

WiFi

WAN

LTE (4G)

Coverage

Frequency (GHz)

Applications

1–3 Mbps

10 m

2.4

Communicate between phones, peripheral devices

802.11

11/54/150/300 Mbps

100 m

2.4

Wireless internet

3GPP

360 Mbps

80 Mbps

30 km

2.4

Commination between people

–

BN

DVB

DVB

300 Mbps

GN

GPS

GPS

50 bps

Up-link

100 km

2.1

Entertainment

100 km

1.5

Localization

References 1. IEEE and IEEE-SA Standards Board, IEEE standard for local and metropolitan area networks. Part 15.6, wireless body area networks. IEEE 1–271 (2012). Available online: https://ieeexplore. ieee.org/document/6161600. Accessed on 10 Dec 2020 2. S. Movassaghi, M. Abolhasan, J. Lipman, D. Smith, A. Jamalipour, Wireless body area networks: a survey. IEEE Commun. Surv. Tutorials 16(3), 1658–1686 (2014) 3. F. Akyildiz, M. Pierobon, S. Balasubramaniam, Y. Koucheryavy, The internet of bio-nano things. IEEE Commun. Mag. 53(3), 32–40 (2015) 4. K.S. Mohamed, Bluetooth 5.0 Modem Design for IoT Devices (Springer Nature, 2021). 5. K. Adarsh, K.M.P. Akshay, J.P. Dhivvya, K.J. Harikrishna, S. Simi, S.N. Rao, Analysis of long range wi-fi backhaul link in maritime environment, in Proceedings of the International Conference on Communication & Signal Processing, Chennai, India (2018), pp. 569–573 6. U. Mehmood, I. Moser, P.P. Jayaraman, A. Banerjee, Occupancy estimation using WiFi: a case study for counting passengers on busses, in Proceedings of the IEEE 5th World Forum Internet Things (WF-IoT), Limerick, Ireland (2019), pp. 165–170 7. S. Bang, C. Ahn, Y. Jin, S. Choi, J. Glossner, S. Ahn, Implementation of LTE system on an SDR platform using CUDA and UHD. Springer J. Analog Integr. Circ. Sig. Process. (AICSP) 78(3), 599 (2014). https://doi.org/10.1007/s10470-013-0229-1

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8. M. Breiling et al., LTE backhauling over MEO-satellites, in Proceedings of the Advanced Satellite Multimedia Systems Conference and Signal Processing. Space Communications Workshop, Livorno, Italy (2014), pp. 1–8 9. www.dvb.org 10. E. Casini, R.D. Gaudenzi, A. Ginesi, DVB-S2 modem algorithms design and performance over typical satellite channels. Int. J. Satell. Commun. Network. 22(3), 281–318 (2004) 11. E. Grayver, Scaling the fast x86 DVB-S2 decoder to 1 Gbps, in Aerospace Conference (AeroConf) (IEEE, 2019), pp. 1–9. https://doi.org/10.1109/AERO.2019 12. B.A. Black et al., Introduction to Wireless Systems (Prentice Hall, Boston, 2008) 13. J.W. Mark, W. Zhuang, Wireless Communications and Networking (Prentice Hall, 2003) 14. L.W. Couch II, Digital and Analog Communication Systems (Prentice Hall, 2007) 15. J.G. Proakis, M.Salehi, Fundamentals of Communication Systems (Prentice Hall, 2005) 16. T.D. Ta, T.D. Tran, D.D. Do, H.V. Nguyen, Y.V. Vu, N.X. Tran, GPS-based wireless ad hoc network for marine monitoring, search and rescue (MSnR), in Proceedings of the International Conference on Intelligent Systems, Modelling & Simulation, Kuala Lumpur, Malaysia (2011), pp. 350–354 17. T. Tsujii, I.G. Petrovski, Digital Satellite Navigation and Geophysics (Cambridge University Press, 2012) 18. E.D. Kaplan, Understanding GPS: Principles and Applications, 2nd edn. (Artech House, 2005)

5G Mobile Communications: Fundamentals, Key Enabling Technologies, Challenges, Opportunities, Future Trends

Motivation: Nowadays, 5G is widely used in many applications such as Internet of Things (IoT) and autonomous driving due to its low latency, fast speed and enhanced capacity. Although there are many related-work that survey 5G. But, to the best of the authors’ knowledge, a lack of comprehensive survey of the 5G remains. This work conduct an in-depth survey on 5G technology and beyond.

1

Cellular System Infrastructure

As can be seen in Fig. 1, geographical regions are divided into cells. We have one base station per each cell. In each cell, there are also many users. Although users in the same cell use different frequency channels, users in different cells may use the same frequency channel (spectrum can be reused). Coverage aspects are shown in Fig. 2. Macrocell covers sections of a city, more than 1 km radius. Microcell covers neighborhoods, less than 1 km. Picocell covers busy public areas such as malls, airports in range of 200 m. Femtocell are inside a home for a range of 10 m. Base station controller and mobile switching center and public switching telephone network connections are shown in Fig. 3, where local connection is done by a wireless link with a small transmission power. And then connection between access points are wired link. Base station controller is connected to mobile switching center using packet-switching. In packet switching (Fig. 4), user can receive data from multiple users at the same time. So, we do not waste the bandwidth as in the case of circuit switching (Fig. 5). In circuit switching, a switching matrix is used to connect any originating party to any destination party. The BSC is responsible for the establishment, release, and maintenance of all connections for cells that are connected to it. Basic types of Mobile communication systems include base/mobile, peer-to-peer, repeater, and mobile satellite systems as depicted in (Fig. 6), [1]. In the 5G core network, © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 K. S. Mohamed, Wireless Communications Systems Architecture, Synthesis Lectures on Engineering, Science, and Technology, https://doi.org/10.1007/978-3-031-19297-5_7

143

144

5G Mobile Communications: Fundamentals …

BS

BS

BS

BS

BS Cell

BS

BS

Fig. 1 Cellular Hexagonal structure

the data are most often structured by means of a standard known as JavaScript Object Notation (JSON), which includes formatting rules for logical values, numbers, character strings, objects and arrays. Actually, there is heterogeneity implied by the coexistence of different mobile network operators.

2

Evolution of Wireless Mobile Communications

Since the arrival of the first-generation network in the early 1980s, a new mobile generation emerging nearly every decade [2–8]. Comparison between different mobile communications generations are summarized in Table 1. Evolution of mobile communications standardization is shown in Fig. 7. The history of each generation can be summarized as follows: A. The first generation of the mobile wireless communication network (1G) 1G was developed around the 1980s in Japan. It is based on analog communications and Frequency Division Multiple Access (FDMA) with a network speed of about 2.4 kbps. It was used for voice calls only. The major disadvantage of 1G was its less capacity and less security. Advanced Mobile Phone System (AMPS) is a first-generation cellular technology that uses separate frequencies, or “channels”, for each conversation. Switching used in 1G is circuit-switched, where signals pass through several switches before a connection is established. And during a call, no other network traffic can use those switches. The aggregate of the world’s circuit-switched telephone networks that are operated by

2

Evolution of Wireless Mobile Communications

145

Satellite

In-building Urban Suburb Global

Picocell

Microcell Macrocell

Global

Fig. 2 Mobile coverage

Fig. 3 Base station controller and mobile switching center and public switching telephone network. The connection between mobile and base station is wireless, but remaining connections are wired such as connections between BSC and MSC. Moreover, mobile can be connected to internet through WiFi. The connection between BSC and MSC is called backhaul

146

5G Mobile Communications: Fundamentals …

Fig. 4 Packet switching. User can receive data from multiple users at the same time. So, we do not waste the bandwidth as in the case of circuit switching

Fig. 5 Circuit switching. User receives data from a user at a time

national, regional, or local telephone operators, providing infrastructure and services for public telecommunication is called the public switched telephone network (PSTN). B. The second generation of the mobile wireless communication network (2G) 2G was introduced in the 1990s in Finland. It is based on digital communications and Global System for Mobile communication (GSM). GSM uses a combination of both

2

Evolution of Wireless Mobile Communications

147

Fig. 6 Mobile radio systems: a mobile/base, b peer-to-peer, c repeater, d mobile satellite

TDMA and FDMA techniques. It provides text services. Its improved version (2.5G) allowed internet access through General Packet Radio Service (GPRS). Switching used in 2G is Circuit-switched networks for phone calls and packet-switched networks for data. Packet-switched networks move data in separate, small blocks based on the destination address in each packet. C. The third generation of the mobile wireless communication network (3G) 3G was introduced in the early 2000s in Japan. It is based on Code Division Multiple Access (CDMA) and Wideband Code division multiple access (WCDMA). It provides faster and robust internet connectivity. 3G gave rise to new services such as mobile TV, video conferencing, and voice over IP (VOIP) services like Skype. Its improved version (3.5G) uses High Speed Packet Access (HSPA) to provide IP-based services.

1990s

500 64 Kbps

1980s

AMPS NMT

–

FDMA

FM (Analog)

–

2 kHz

–

–

2.4 Kbps

Analog

Analog voice calls only

Deployment ~

Standard

Technologies candidates

Multiple access technique

Modulation

Spectrum (GHz)

Bandwidth ~

Internet service

Latency (ms)

Average speed/data rate

Voice

Features/differentiators/advantages Digital voice calls + text messages (SMS) + MMS

Digital

–

64 kHz

–

High speed + IoT + AR + autonomous driving + cloud support + eMBB + mMTC + URLLC

Mobile broadband + packet-based communication + faster mobile web access + HD/real time video streaming + Gaming + mobile TV

Video calls + internet + roaming + positioning + multimedia

400 Mbps

1

WWWW

1 GHz

0.4–52.6

256 QAM

NOMA

mm-waves (above 6 GHz), mMIMO

3GPP IEEE

2020s

5G

Voice over long-term evolution – (VoLTE)

100 Mbps

10

Ultra broadband

200 MHz

0.4–6

QPSK, 16/64 QAM

OFDMA

–

LTE

2010s

4G

Digital

10 Mbps

100

Broadband

20 MHz

0.8–2.1

QPSK

CDMA

FDMA + TDMA PSK (Digital)

–

UMTS WCDMA

2000s

3G

–

GSM GPRS

2G

1G

Table 1 Comparison between different mobile communication generations

(continued)

holographic teleconferencing + union of/combination of (eMBB, mMTC, URLLC)

–

1 Gbps

0.1

–

1 THz

0.4–114

APSK/512 QAM

RSMA

Terahertz (THz) communication

–

2030s

6G

148 5G Mobile Communications: Fundamentals …

No – – – –

–

–

BCH codes

No

–

No

–

–

–

coverage/connectivity (devices/km2 )

Mobility (km/h)

Channel coding

Beamforming

QoS

Network slicing

Energy efficient

Jitter

Reliability

No

BCH codes

–

–

Circuit, packet

Circuit/PSTN

Switching network

Limited data rate

2G

Poor spectral efficiency, poor voice quality, large size of the cell phone, no security

1G

Weakness/disadvantages

Table 1 (continued)

–

–

–

No

Bearer-based

No

Turbo

–

–

Packet

Slow internet access, insufficient data rate for real time applications

3G

–

–

–

No

Bearer-based

Data only

Turbo

Enhanced LDPC/polar

1000

107

Packet

High cost

6G

1 µs 1–10−9

1–10−5

1 Tb/J

Yes

Flow-based

–

–

Yes

Flow-based

Data and control Data and control

LDPC (data)/polar (control)

500

106

105 350

Packet

Needs greater number of access points

5G

Packet

Short time of battery life, not enough connectivity/data rate for IoT, AR

4G

2 Evolution of Wireless Mobile Communications 149

150

5G Mobile Communications: Fundamentals …

Fig. 7 Evolution of mobile communications standardization

D. The fourth generation of the mobile wireless communication network (4G) 4G was introduced in 2010 in Finland. It is based on Orthogonal Frequency division multiplexing (OFDM), Wireless Interoperability for Microwave Access (WiMAX), and Long Term Evolution (LTE) [9]. It increases the bandwidth, so it can be used with the applications that require very high speed such as online gaming and high definition video streaming. 4G allowed for near real-time, intensive data exchange between end-users [10, 11]. E. The fifth generation of the mobile wireless communication network (5G) 5G was introduced in 2019 in Finland. 5G systems are bringing a radical shift in the way both the access and the core networks are designed. The 5G networks would greatly improve efficiency over 4G. It supports higher bandwidth, lower latency and offers seamless connectivity to multiple phones, compatible with different technologies. 5G utilizes mm-wave communications to use the very high end of the wireless spectrum, where large amounts of bandwidth remain unused.

3

5G KPIs

151

F. The sixth generation of the mobile wireless communication network (6G) With the highly growing bandwidth and traffic demand for wireless applications, new technologies related to 6G are emerging. 5G and B5G (future wireless communication systems) supports many use-cases applications such as autonomous vehicles, smart homes, smart cities, IoTs (much higher bandwidth and connectivity to billions of devices), industry 4.0, society 5.0, and e-health.

3

5G KPIs

Major 5G key performance indicators (KPIs) are summarized in Table 2 and Fig. 8. Table 2 5G KPIs terms definitions Speed: Peak data rate (Gbit/s)

Max rate per user under ideal conditions

User experienced data rate (Mbit/s)

95% Rate across the coverage

Latency (ms)

Radio contribution to latency between sender and receiver

Mobility (km/h)

Max speed at which seamless handover and QoS is guaranteed

Connectivity: connection density/coverage (devices/km2 )

Devices/users per km2

Energy efficiency (bits/Joule)

Network bits/Joule, User bits/Joule

Spectrum efficiency (Throughput/Hz/cell)

Throughput per Hz per cell

Area traffic capacity (Mbit/s/m2 )

Throughput per m2

Spectrum and bandwidth Flexibility

Ability to operate at different frequencies and channel bandwidths

Reliability

High availability

Resilience

Continue working in face of disasters

Security and privacy

Confidentiality, integrity, authentication, protection against hacking, denial of service, man-in-the-middle attacks

Operational lifetime

Long battery life

Quality of service (QoS)

It is a mechanism controls the performance, reliability and usability

152

5G Mobile Communications: Fundamentals …

Fig. 8 5G KPIs with the major 5G applications

A. Speed The expected speed of 5G is expected to be 100 times faster than 4G. With such a speed, you can watch 4k video directly without buffering. B. Latency Latency is the time elapsed between service request and its availability. Low Latency (~ 1 ms) is one of the key features of 5G. With such a speed, you can watch 4k video directly without jitter. This low latency is suitable for critical applications such as robots monitoring patients.

4

5G Opportunities/Applications

153

C. Energy efficiency Energy efficiency is one of the major aims of 5G to support IoT applications. D. Connectivity 5G networks consist of switched wireless systems which uses orthogonal frequency division multiplexing (OFDM) which has 20 Mbps to a distance of 2 km. E. Reliability 5G technology promises to improve services like high reliability, speed and energy efficiency even in dense areas like bigger cities. F. Mobility Mobility is maximum speed at which seamless handover and QoS is guaranteed. G. Security 4G had security vulnerabilities. 5G networks will transport large amount of IoT data and therefore sensitive data should be encrypted and authorization of the entity is even more important. H. Bandwidth BW is another very important and critical factor which makes 5G superior to 4G. The big increase in bandwidth also means that 5G will be able to handle up to one million devices per square kilometer, another 10 fold increase over 4G.

4

5G Opportunities/Applications

Applications drive Technical Requirements. There are different applications according to different requirements. Different applications need different data rates, different form factor, and different mobility range and have different cost limitations and different power budget. Communication now includes both human–machine and machine-to-machine interaction. The different services may mapped to different standardized slice types optimized for the needs of each service. Three basic requirements of performances considered

154

5G Mobile Communications: Fundamentals …

in 5G services are high data rate, low transmission latency, and massive connectivity, which address the most urgent wireless communication issues for present demands. 3GPP specified three service types illustrated below. The KPIs for these applications are summarized earlier in Fig. 8. A. Human-Centric Communications Enhanced Mobile Broadband (eMBB): Better mobile phones and hot spots. High data rates, high user density. B. Human and Machine Centric Communication Ultra-Reliable and Low-Latency Communications (URLLC): Vehicle-to-Vehicle communication, Industrial IoT, 3D Gaming. 5G enables vehicle to everything (V2X) communications (Fig. 9) which are Vehicle to Vehicle (V2V), Vehicle to Network (V2N), Vehicle to Infrastructure (V2I) and Vehicle to Pedestrian (V2P) [12–14].

Fig. 9 5G illustration of vehicle to everything communications

6

5G Momentum: 5G Key Wireless Enabling Technologies

155

C. Machine-Centric Communication Massive Machine Time Communications (mMTC): Very large number of devices, low data rate, low power. IoT with long battery life time. Addition to GSM, LoRa, Zigbee, etc.

5

5G Challenges

5G is designed to work with diverse applications and is meant to be a platform for many wireless Technologies which will be co-existing. However, this implies many challenges: • 5G utilizes mm-wave communications to use the very high end of the wireless spectrum, where large amounts of bandwidth remain unused. While mm-wave communications enable faster network speeds, but they are less reliable at long distances and are more susceptible to blockage. Therefore, 5G networks require a greater number of access points than 4G networks. Moreover, there are many challenges when we work above 6 GHz such as increasing free-space loss and Doppler shift which affects the mobility and the antenna design becomes more expensive. • Security challenges [15–17]. • Many of the old devices would not be competent to 5G, hence, all of them need to be replaced with new one.

6

5G Momentum: 5G Key Wireless Enabling Technologies

5G introduce the concept of World Wide Wireless Web (WWWW). All the 5G enabling technologies quarantine improving data rate, reducing latency and guaranteeing reliability and increasing connectivity (Table 3). Enabling 5G requires changes in Electromagnetic bands, network architecture, antenna technology, coding, modulation, multiple access, duplexing. Moreover, it requires deploying machine learning [18]. 5G Enabling Technologies are summarized in Fig. 10. Table 3 Features and enabling technologies

Feature

Enabling technology

Improving data rate

MIMO, NOMA

Reducing latency

ML-based channel estimation

Reliability

UDN

Increasing connectivity

MIMO [19]

156

5G Mobile Communications: Fundamentals …

5G

Electromagnetic bands

mmWaves

FTN

Network architecture

UDN

ORAN

SDN

Antenna technology

Slicing

Beamforming

mMIMO

Coding, modulation, multiple access, duplexing

Machine learning

NOMA

Channel estimation

Fig. 10 5G Enabling Technologies

A. Changes to Electromagnetic Bands (1) mm-Waves Band Use more bandwidth to increase the capacity without the need to increase number of base stations. Moreover, the current frequency bands used for 4G are quite crowded. This poses a big challenge for future telecommunications products, as bandwidth is a key resource. So, a push for higher frequencies is made [20]. (2) Flexible Spectrum Access/Sharing: Cognitive Radio Cognitive radio refers to a transceiver that detects unused frequency bands in the spectrum for communication. It’s based on software defined radio (SDR), which is the principle of making most of the radio functions “software based” rather than “hardware based”, which implies using DSP’s or FPGA’s instead of using Application Specific IC’s in Radio Transceivers. This provides flexibility in re-configuration of any transceiver. SDR advantages are design flexibility, reliability, upgradability, re-usability, re-configurability, lower cost. Figure 11 graphically contrasts traditional radio, software radio, and cognitive radio. In the recent past, Cognitive Radio (CR) technology has been considered an attractive solution for exploiting limited spectrum resources. The CR system implements negotiated or opportunistic spectrum sharing over a wide frequency range covering multiple mobile communication standards. Thus, the CR link intelligently detects the usage of a frequency segment in the radio spectrum, and jumps into any temporarily unused spectrum rapidly without interfering with communication between other authorized users. This technology is very promising for the friendly coexistence of the heterogeneous wireless networks, i.e.,

6

5G Momentum: 5G Key Wireless Enabling Technologies

157

cellular, wireless PAN, wireless LAN, and wireless MAN, etc. In spectrum sharing, choosing the radio transmit power that causes minimal interference to primary users presents the crucial challenge. Opposite to UWB, a cognitive radio approach does not necessarily limit the transmission power, but rather attempts to share the spectra through a dynamic avoidance strategy and would use higher transmit power in “white spaces” to maximize capacity [21, 22]. The general approach for physical layer sensing, that we will follow, relies on the estimation of a specific primary user property or parameter (e.g. energy or a pilot signal). This estimated parameter is then used in the detection process, implemented as a simple hypothesis testing. A major challenge in the spectrum sensing design is the requirement to detect very weak signals of different types in a minimum time with high reliability. Cognitive radios support new users in existing crowded spectrum without degrading licensed users. • Energy Detection The most basic approach for detecting signals in the presence of noise is based on energy measurement. Also, it is the most general technique since it applies to any signal type. It requires minimum information about the signal, including only signal bandwidth and carrier frequency. The energy detector is implemented on the FPGA of the reconfigurable

Fig. 11 Logical diagram contrasting traditional radio, software radio, and cognitive radio

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Fig. 12 Energy detector implementations: a narrowband architecture, b wideband architecture

wireless modem. It is designed using 1024 point FFT with a fully parallel pipelined architecture for the fastest speed (Fig. 12). • Pilot Detection The benefit of pilot signals, if they are perfectly known to cognitive radio sensor, is that it can be processed coherently. Coherent processing achieves the best possible robustness with respect to noise. (3) Faster-Than-Nyquist (FTN) Faster-Than-Nyquist (FTN) is a technique that can improve the spectral efficiency of communication systems by making better use of available spectrum resources at the cost of inter-symbol interference (ISI) and inter-carrier interference (ICI). B. Changes to Network Architecture (1) Ultra-Dense Networking (UDN) It is one way to increase capacity by densification. The mm-Wave have a range of about 1 mile (1.6 km), requiring many small cells, and have trouble passing through some types of building walls. In UDN, there are comparably many number of base stations equal number of mobile terminals. So, some of the base stations will be turned off to save energy or not to generate any interferences like this.

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(2) Network Slicing 5G network slicing is a network architecture that enables the multiplexing of virtualized and independent logical networks on the same physical network infrastructure. In network slicing the network infrastructure is split into logical instances, i.e., network slices, each tailored to a dedicated service and running in a cloud infrastructure. A network slice could span across multiple domains, i.e., radio access network, transport network, and core network, and could be deployed across multiple operators. It comprises dedicated and/or shared resources and can be completely isolated from the other network slices to fulfill service level agreement. (3) Software Defined Network (SDN) SDN is a network architecture that breaks the vertical integration in traditional networks to provide the flexibility to program the network through (logical) centralized network control. It separates the control plane and the data plane, in which the control plane or the control function is logically centralized at one or a set of control entities called SDN controllers, while the data plane is simplified and abstracted for network applications and services requesting through the SDN controllers. (4) Open Radio Access Network (O-RAN) The radio access network (RAN) architectures that have been mainly deployed for 5G are the C-RAN, which stands for centralized, clean, cloud, and collaborative RAN, and the distributed RAN (D-RAN). RAN is another name for base station. C-RAN has evolved to novel Virtual RAN (V-RAN) and Open RAN (O-RAN) [23, 24]. Evolution of C-RAN can be shown in Fig. 13. O-RAN means that any vendor’s equipment would work on any part of the network regardless of whose core was managing the network [25]. C. Changes to Antenna Technology With the increasing demand for further improving the channel capacity and efficiency of mobile communication system, the design of antennas for 5G base stations and mobile terminals will be more challenging.

Fig. 13 Evolution of C-RAN

C-RAN

V-RAN

O-RAN

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Fig. 14 Beamforming

(1) Beamforming in 5G mm-wave radio networks To overcome the severe path loss. As the higher frequencies in 5G applications have poorer propagation characteristics. Beamforming is a technique linked to spatial diversity. It is a spatial filtering technique. Analogous to transitioning from a lightbulb to a laser beam, this method focuses the RF signal in three dimensions between base stations to end-devices, allowing for a narrower signal and de-confliction within the spectrum. Traditional base stations emit in sectors, roughly in thirds of a sphere; beamforming will instead transmit directional signals based on the relative geolocation of each device. Beamforming transceiver is shown in Fig. 14. Antenna coordination for directional beams is enabled with an aid of channel state information (CSI) feedback, and thus it is essential to efficiently deliver such information from a beam-formee to a beam-former. (2) Massive Multiple Input Multiple Output (mMIMO) With the development of communication, the multiple input- multiple-output (MIMO) technology [26], as one major breakthrough in modern wireless mobile communication systems, is used to improve the communication capacity of the entire system, because it can expand the channel capacity, reliability, and spectrum resource utilization of mobile communication systems [27]. Given an arbitrary wireless communication system, we consider a link for which the transmitting end as well as the receiving end is equipped with multiple antenna elements. The idea behind MIMO is that the signals on the transmit (Tx) antennas at one end and the receive (Rx) antennas at the other end are “combined” in such a way that the quality (bit-error rate or BER) or the data rate (bits/sec) of the communication for each MIMO user will be improved. MIMO systems can be viewed as an extension of the so-called smart antennas. Massive MIMO is the advancement of contemporary MIMO systems used in current wireless networks, which groups together hundreds and even thousands of antennas at the base station and serves tens of users simultaneously [28, 29].

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Massive MIMO, an extension of MIMO, expands by adding a much larger number of antennas at the base station. Before massive MIMO, MIMO uses multiple antennas at the transmitter and receiver sides to send and receive signals by exploiting multipath propagation. The massive number of antennas helps focus on energy, which brings drastic improvements in throughput and efficiency due to its more responsive behavior towards devices transmitting in higher frequency bands. D. Changes to Coding, Modulation, Multiple Access, Duplexing (1) Non-orthogonal Multiple Access (NOMA) Traditional orthogonal multiple access (OMA) schemes may struggle to accommodate the massive number of 6G connections, since these techniques divide a resource block (RB), time, frequency, or code, between users equally without taken into consideration the variations of their channel conditions. In order for 5G to support large-scale heterogeneous traffic and users, new modulation and multiple access (MA) schemes are being developed [30]. NOMA boosts spectral efficiency and system capacity. NOMA scheme utilizes the power allocation technique in the power domain instead of the time and frequency multiple access, in which the symbols of several users are scaled on the base-station (BS) according to their channels conditions, i.e. the attenuation factor caused due to path loss of each user’s channel, then all the scaled symbols are added together and transmitted as one symbol called NOMA symbol. In other words, a user of far distance from the BS will be given more power than a user with near distance. At the far-user (FU), the receiver will deal with near user (NU) signal on the NOMA symbol as a noise, while at the NU, successive interference cancellation (SIC) is required to remove the FU power since the letter is bigger than the intended NU power. In brief, SIC is simply utilized by detecting the FU signal at the NU terminal and subtract it from the overall NOMA signal to obtain the NU signal [11, 19, 31–35]. NOMA compared to Different multiple access techniques can be shown in Fig. 15. (2) Full-Duplex Today’s cellular base stations cannot multitask. They can either transmit or receive data at any given time. Full Duplex refers to the transmission of data in two directions simultaneously. Like on a telephone on which two people can talk at once. It enables simultaneous transmission and reception over the same frequency band. In this way, the spectral efficiency can be improved significantly compared with half-duplex (HD).

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Fig. 15 NOMA compared to Different multiple access techniques

E. Machine Learning Applications in 5G AI/ML can be used to address communication problems of high complexity such as [35]: • Many problems in 5G wireless communications are nonlinear thus can b approximated. NN-based machine learning can be used for such problems. • Machine learning in 5G wireless communications can address the optimal physical layer design, complex decision making, network management, and resource optimization tasks (spectral management), change in processes may be evaluated to achieve minimal energy evaluation, network traffic classification [36]. • ANN can be used for denoising signals and enhancing the SNR instead of traditional approach of using constellation de-mapping and LDPC decoders [37]. • Deep Learning-Based NOMA, Deep Learning-Based Massive MIMO, Deep LearningBased mm-Wave, Deep Learning for Channel Estimation [38, 39]. • AI for Dynamic Spectrum Sharing. • ML-based channel estimation. • AI to enhance/optimize handover.

References

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Paving the Way Towards 6G

Motivation: With the highly growing bandwidth and traffic demand for wireless applications, new technologies related to 6G are emerging. 5G and B5G (future wireless communication systems) supports many use-cases applications such as autonomous vehicles, smart homes, smart cities, IoTs (much higher bandwidth and connectivity to billions of devices), industry 4.0, society 5.0, and e-health. There are several challenges facing the research and development of 6G systems. This paper covers requirements, different key enabling technologies for 5G and beyond, architectures, challenges, and future perspectives. Moreover, it highlights drivers and motivation for B5G. List of important acronyms is given in Table 1.

1

6G Opportunities/Applications

A. E-Health In various health operations such as emergency care, medical checkups, cleaning contaminated floors, and the supply of medication in rural areas, autonomous robotics can be used. B. Holographic Transmissions Holography is a technique for capturing an object’s full 3D image. Video calling and video recording, such as movies, will be replaced by holography.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 K. S. Mohamed, Wireless Communications Systems Architecture, Synthesis Lectures on Engineering, Science, and Technology, https://doi.org/10.1007/978-3-031-19297-5_8

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Table 1 List of important acronyms in 6G communications Acronym

Definition

3GPP

3rd Generation partnership project

AI

Artificial intelligence

AR

Augmented reality

eMBB

enhanced mobile broadband

eRLLC

Extremely reliable low-latency communication

mMTC

Massive machine time communications

IoT

Internet of things

ML

Machine learning

NOMA

Non-orthogonal multiple access

VLC

Visible light communication

C. Communication in Space and Deep Sea 6G will expand the range of activities throughout the globe with the availability of easy and effective tools of communication. Deep-sea exploration such as oil exploration and mineral exploration can become a reality. D. Industry 5.0 6G will bring a new industrial revolution termed as beyond the Industrial 4.0. Industry 5.0 refers to people working alongside robots and smart machines to add a personal human touch to Industry 4.0 pillars of automation and efficiency. A massive number of things in Industry 5.0 are connected either using wired or wireless technologies to provide various applications and services that are enabled by a complete integration of cloud/edge computing, big data, and AI [1, 2]. E. Digital Twin Digital twin is used to create detailed virtual copy of a physical object to be used to manufacture multiple copies of an object with full automation and intelligence [3].

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2

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6G Potential Key Wireless Emerging Enabling Technologies

6G is not an extension for 5G. Instead, it is a revolution technology to support new requirements (Eq. 1). Thus, there are many new potential enabling technologies for it [4– 6]. Future 6G wireless systems will rely on multiple technologies which will be discussed in the subsequent sub-sections. Comparative analysis is shown in Table 2. 5G Enabling Technologies are summarized in Fig. 1. ) ( broadband + large coverage + Intelligence 6G = Ultra +massive connectivity + low energy + low latency (1) However, massive connectivity requirements in 6G will result in high interference, which will result in a significant performance-bottleneck. Intelligent beamforming CAN reduce sensitivity to interferences. A rapid channel coding and decoding process can support low-latency services. Table 2 Comparative analysis of different 6G enabling technologies Technology

Pros

Cons

Quantum communication

High performance

Complex

Reconfigurable intelligent surfaces • Low complexity and cost • Power efficient

Complex

New spectrum

Higher bandwidth availability

Low penetration power Cost

New multiple access

Improved network efficiency

Internet-of-everything (IoE)

Low latency and higher data rates Low energy efficiency

Blockchain [7]

Integrity

Privacy concerns

6G

Electromagnetic bands

THz Band

Blockchain -Spectrum Share

Network architecture

WPT

Heteroge nous

Fig. 1 6G enabling technologies

Coding, modulation, multiple access, duplexing

Antenna technology

Intelligent Beamforming

Super mMIMO

LIS RSMA

Index Modulati on

Machine learning

Channel estimation

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Fig. 2 THz band

A. Changes to Electromagnetic Bands (1) New frequency bands: Terahertz (THz) communications With the limited spectrum resources for 5G, industries, we need new radio spectrum to meet 6G capacity demand. High frequency millimeter wave near 100 GHz to THz is one of the key spectrum resources for the sixth generation (6G) mobile communications system [8–11]. The unique characteristics of the terahertz band, such as high path loss, scattering, and reflection, pose many new challenges that need to be addressed before achieving the Tbps (BW > 50 GHz). Terahetz band versus mmWave band is shown in Fig. 2 [12, 13]. THz communications employ highly directional transmission, inter-cell interference can be decreased while decreasing the chances of eavesdropping as well and thus providing security. The new frequency band is not a replacement for 4G/5G bands. On the contrary, it is a complementary for them. THz band can be used for indoor/outdoor communication scenarios [14, 15]. The Terahertz band (0.3–10 THz) is promising for ultrafast wireless communications. (2) Blockchain-based spectrum sharing Spectrum management plays a key role in the deployment of new wireless technologies. Databases continue to be important enabler for spectrum sharing by offering the platform where data of spectrum use is stored and managed. Various technologies can be deployed on top of databases such as blockchain that are studied for spectrum management [16]. It can be easily integrated into the walls of the building [17–19].

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B. Changes to Antenna Technology (1) Super massive MIMO Enlarge the antenna array concept. Massive MIMO systems, is introduced as one of the vital technologies to attain high spectral efficiency, reliability, and power-saving [20]. (2) Large intelligent surfaces (LISs) An LIS is an artificial surface made of electromagnetic materials, which can change the propagation of incoming and outgoing radio waves for directional signal enhancement or nulling. It is significantly different from other traditional technologies, such as massive MIMO. LISs are made of low-cost passive elements that do not require any active power source for transmission. Altering surface impedance can phase-shift reflection (Figs. 3 and 4) [21]. In the conventional wireless communication architecture, the wireless channel is random and uncontrollable, which becomes the ultimate bottleneck of achieving highcapacity and ultra-reliable communications. The wireless channel fading is mainly due to the reflections, diffractions, and scattering in the surrounding environment. Therefore, RIS is proposed to implement a smart radio environment and enhance the wireless channel status by controlling the reflections of wireless signals. The RIS is motivated by the metasurface, which is a two-dimensional planer metamaterial. The metamaterial breaks traditional Snell’s law to change the amplitude and phase of reflected wireless signal. Moreover, the metamaterial can be achieved by microelectromechanical system (MEMS), positive-intrinsic-negative (PIN) diodes, graphene, and other digitally controllable materials. Thus, the properties of reflected signal are tunable through field-programmable gate array (FPGA) controller. A large number of metamaterial units (a.k.a. reflecting element) compose the RIS in a two-dimensional plane, which arbitrarily controls the wireless signals [22]. (3) Intelligent beamforming Machine learning can be used with beamforming to add some degree of intelligence. (4) Reconfigurable meta-surface (Antennas) To improve reliability, reconfigurable meta-surface (Antennas) can control the propagation environment during communications [23, 24].

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Fig. 3 Large intelligent surfaces

Fig. 4 LIS use case

C. Changes to Coding, Modulation, Multiple Access, Duplexing (1) Orbital angular momentum (OAM) Communication: OAM-based multiplexing Unlike the traditional plane electromagnetic wave-based signals, OAM has a phase rotation factor. So, it can have an unlimited number of Eigen states which are orthogonal to each other, and therefore, multiple channels are allowed to increase the transmission capacity and spectral efficiency.

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Fig. 5 Evolution of duplex

(2) Advanced duplex: Free Duplex Flexible duplex allows to dynamically allocate the resources in both time and frequency domain simultaneously rather than static TDD and FDD to address asymmetric traffic where wireless data traffic is asymmetric in nature. It is mainly used to solve the unbalanced spectrum requirements between different nodes in the same network and between the transceiver and receiver links of the same node. There is no FDD/TDD differentiation, but a flexible and self-adaptive scheduling mode of flexible duplex or full duplex, according to the service requirements between the transceiver and transceiver links. In previous wireless generations (1G–5G), wireless systems were using either fixed duplexing (TDD/FDD) or flexible duplexing. Whereas, 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. Evolution of Duplex is shown in Fig. 5 [25]. (3) Rate splitting multiple access (RSMA) Rate-Splitting Multiple Access (RSMA) is a multiple access scheme based on the concept of Rate-Splitting (RS) and linear precoding for multi-antenna multi-user communications. RSMA splits user messages into common and private parts, and encodes the common parts into one or several common streams while encoding the private parts into separate streams. The streams are pre-coded using the available (perfect or imperfect) Channel State Information at the Transmitter (CSIT), superposed and transmitted via the Multi-Input Multi-Output (MIMO) or Multi-Input Single-Output (MISO) channel. All the receivers then decode the common stream(s), perform Successive Interference Cancellation (SIC) and then decode their respective private streams. Each receiver reconstructs its original message from the part of its message embedded in the common stream(s) and its intended private stream [26].

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(4) Index modulation and spatial modulation They utilize the indices of resource blocks and antennas to deliver extra bits. Spatial modulation (SM) is a powerful tool that can be exploited to simplify the MIMO transceiver design. One antenna is active during the transmission and the remaining are silent and thus, only one RF chain is needed. Part of the input data bits is mapped into the index of the active transmit antenna and the other part is mapped into M-ary modulation symbol to be transmitted from the active antenna. After that, the receiver applies maximum likelihood (ML) detector to jointly detect the index of the active transmit antenna and the M-ary symbol. High capacity can be achieved due to the spare information bits’ transmission. Due to the fact that sending of these spare bits does not consume any power and spectrum resources, index modulation (IM) has both high spectral efficiency and high energy efficiency simultaneously [27, 28]. D. Changes to Network Architecture (1) Heterogeneous Network: Space-air-ground-sea integrated (SAGSI) network There are several compelling advantages in integrating the ground, air and space networks. Firstly, the available frequency bands may be integrated with the aid of wideranging spectrum sharing; Secondly, the flexible deployment of aerial platforms may be combined with those of satellites for providing ubiquitous high-quality connectivity [29]. Additional media-based networks must also be integrated into 6G systems, including sonar-based underwater networks, magnetic-induction-based underground networks, molecule-based Nano-IoT, and the physiological-tissue based body access network (Figs. 6 and 7). Satellites, high and low altitude platforms, drones connected to balloons (especially for rural areas), aircrafts, and airships are being considered as candidates for deploying 6G wireless communications [30]. 6G is also aiming to provide underwater communication. In reality, the underwater environment is a different scenario compared to air or space. The underwater environment is unpredictable and complex due to high signal attenuation, physical damage to equipment and complicated network deployment. Radio signals are highly attenuated in salt water. Therefore, acoustic communication is the only option for communication [31]. The satellite-based communication systems have wide coverage and can provide lowspeed or high-speed data services depending on the bandwidth. However, satellite-based communications are easily affected by climate and the marine environment, resulting in low reliability [32, 33]. Since LEO satellites typically have lower altitude orbits compared to GEO and MEO, LEO communication is highly attractive to the industry owing to its ability to provide latency on the order of tens of ms [34, 35].

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Fig. 6 Space-air-ground-sea integrated (SAGSI) network. A schematics of the earth coverage in 6G era. The idea of 6G is to realize the integration of space, heaven and earth

Fig. 7 Combining mobile and satellite networks for coverage

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Paving the Way Towards 6G

(2) Wireless power transferring (WPT) WPT network provides wireless power for multiple devices in an indoor environment, where the wireless power transmitters can be embedded in light bulbs on the ceiling. The network can be deployed in public places such as coffee shops, airport terminals and theatres to provide charging services covering the room. Moreover, MPN infrastructural network can provide mobile power for electric vehicles if deployed in parking lots. The wireless power transmitters may also be placed on buildings, utility poles or trees to provide power supply outdoors. IoT devices in 6G will be more power hungry because of the huge computation demands for AI processing. On the other hand, energy harvesting from ambient RF signals may even become a viable power source for low-power application [36]. Wireless power transfer has emerged as an effective solution to recharge low-power devices with limited lifetime. E. Beyond Silicon: New Communications Paradigms in 6G (1) Quantum Communications (QC) Quantum communication is foreseen to play a crucial role in realizing secure 6G communications. Quantum communication increases data rate and security. A number of works already demonstrated initial practical implementation of quantum key distribution (QKD) and associated protocols. Another attractive feature of quantum communication is that it is suitable for long distance communication [37]. (2) Molecular Communications (MC) Information transmission using chemical signals or molecules (inspired by nature). Molecular communication can be used where the use of EM waves becomes challenging as in Human body or Pipe networks [38]. TX and RX could be biological (synthetic cells) or electronic devices (spray, sensor). The modulation can change concentration or release time. An example is shown in Fig. 8. (3) Optical Communications The entire signal processing chain of wireless communication is gradually migrating to the photonic domain. As conventional radio-frequency-based wireless communication is now seriously challenged by the overcrowding RF spectrum, leading to insufficient capacity to support the ever-increasing wireless data traffic. The idea of optical wireless communication has been presented as a promising solution for obtaining larger bandwidth and

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Fig. 8 Molecular communication example

offsetting the frequency spectrum crowding problem [39]. There are different types of optical communications: • Visible Light Communications (VLC): Visible light communication (VLC), a wireless communication technology, does not require transmission media such as optical fiber or physical lines. It utilizes visible light to transmit information and data, instead of occupying radio frequency resources. Communication systems based on visible light commonly use LEDs with high response sensitivity as the transmitter signal source, and loads signals on the LED-driven circuit through intensity modulation. VLC uses LED in order to serve the dual purpose of high-speed data transmission as well as lightning. Employing VLC in short-range links gives many advantages over traditional radio communications such as extremely high bandwidth as the spectrum is unlicensed and free. VLC is envisioned to provide data rates in the range of hundreds of gigabits per second or even reach terabits per second speed when 6G gets deployed [40]. VLC is optical free-space communication, and in the optical wireless communication system, the Line of Sight (LOS) is the common link between two points. In a direct and unobtrusive path, the transmitter directs the visible light beam to the receiver. The data transmission prototype adopts modulation of intensity and direct detection. • Near Infra-Red Communications: using laser in the band of (750–1600 nm). • Ultra-Violet Communications: using laser in the band of (200–280 nm). F. Machine Learning Applications in 6G More intelligence is needed. A complex and heterogeneous network such as 6G will require an AI paradigm which is self-aware, self-adaptive, proactive, and prescriptive in

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Paving the Way Towards 6G

order to make the network intelligent. AI-bases wireless transceiver versus conventional one is depicted in Fig. 9. ML algorithms should be deployed and trained at different levels of the network: management layer, core, radio base stations, and as well as in mobile devices. Given sufficient samples, the DNN is able to extract important features from network inputs and realize end-to-end learning for predicting or regressing. Federated learning is a promising learning mode which assure privacy where learning the model parameters is performed over a central unit, either a data center or an edge host, while the data are kept in the peripheral nodes. In centralized federated learning, the devices do not send their data to any remote

(a)

(b) Fig. 9 a Conventional wireless transceiver, b AI-based wireless transceiver

2

6G Potential Key Wireless Emerging Enabling Technologies

177

server. They only share local estimates of the parameters to be learned. Each device can boost its performance without exchanging data, thus preserving privacy [41, 42]. The huge advances in technological applications such as unmanned aerial vehicles (UAVs), massive robotics, vehicle-to-everything (V2X), and Internet of Things (IoT) will require more advanced wireless communication systems to meet their massive data exchange requirements and demands. Machine learning will Play a vital role in the future wireless communication networks. Moreover, it will be an enabling technology for several advanced services and functions. We can deploy ML technology to 5G/6G wireless communications to address challenges in physical layer design, MAC layer, Network layer, and Application layer. In this paper, we survey the different opportunities of deploying ML in wireless communications [20, 43–48]. AI is a key enabler for the next generation 6G mobile networks. There are many levels where ML can be integrated into 6G wireless communication system. Table 3 summarize ML applications at different layers of the communications protocol stack. Taking into consideration that power, cost and size are always important in implementations of neural networks. Table 4 shows different ML techniques and their wireless communication applications. Medium access control (MAC) layer of a cellular networks performs tasks such as user selection, user pairing for MIMO systems, resource allocation, modulation and coding scheme selection, power management and control of uplink transmissions and random access, mobility and handover control. The applications of ML for 6G can be: • ML can be trained to learn the characteristics and model systems that cannot be presented by a mathematical equation. • Power control, beamforming, and modulation and coding scheme selection. • Interference detection and mitigation, uplink and downlink reciprocity in FDD, channel prediction. Deep learning (DL) uses cascaded neural network layers to extract features from the input data automatically and make a decision. The most important deep learning architectures that are suitable for wireless communications domain [12, 13, 16, 38, 49–51]: • Multi-layer perceptrons (MLPs): are the basic models that are generally used in many learning tasks. • Convolutional neural networks (CNN): use convolution operation to reduce the input size are often used in image recognition tasks. • Recurrent neural network (RNN): are most suitable for learning tasks which require Sequential models. • Autoencoder (AE): are used for dimension reduction. • Generative adversarial networks (GANs): are used to generate samples similar to the available dataset.

ML application

Physical layer

MAC layer

Network layer

–

Resource allocation

– – Since DNN has to be trained offline because of requirements on long training period and large training data, mismatches between the real channels and the channels in the training phase may cause performance degradation. Online training might be promising approaches to overcome this problem

Synchronization

Positioning

Channel estimation/prediction

(continued)

There are no clear findings yet

Channel coding

modulation and coding scheme selection –

Both spectrum reuse and spectrum sharing will cause inter-user interference and lead to errors unless signal power is under constraints. Effective control of the signal power can reduce inter-user interference, and it will increase system throughput

Power management

–

Traffic prediction –

–

Anomaly detection

Flexible duplex

–

Most of the techniques are in the initial phase of research

Notes

Routing

Application layer Performance management

Layer

Table 3 6G: ML applications at different layers of the communications protocol stack

178 Paving the Way Towards 6G

Layer

Spectrum sharing

Frequency spectrum is the most valuable and limited resource among wireless communication resources. Various ML techniques are proposed to compensate the huge traffic demands and efficiently manage spectrum resources

Beamforming improves the signal-to-interference-noise ratio (SINR) at the intended users and reduces the interference to other users by improving the directionality of the transmit signal. The beamforming is performed by multiplying the transmit signals by beamforming coefficients, which are calculated according to the channel status. Machine learning can be incorporated in such system to improve the efficiency of the beamforming calculation and reduce the computational complexity

Beamforming

Optimization

Notes

ML application

Table 3 (continued)

2 6G Potential Key Wireless Emerging Enabling Technologies 179

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Table 4 6G: different ML techniques and their wireless communication applications ML techniques

Learning model

Application

Supervised

Support vector machines

Predict propagation path loss in wireless networks

Unsupervised

K-means clustering

Deciding which low-power node is upgraded to a high-power mode in order to reduce latency and the power node in wireless cellular networks

Reinforcement

Q-learning

Enables users to predict their return function

Table 5 Comparison between 5 and 6G in terms of KPIs KPI

5G

6G

Power

Low

Ultra-low

Connected devices

Massive

Super massive

Energy

–

1 pj/bit

Security

Medium

high

QoS

URLLC

Fully intelligence URLLC

• Reinforcement learning (RL) models enable BSs to learn from the real-time feedback of dynamic/uncertain environment and mobile users, as well as from their historical experiences

3

6G KPIs

The same KPIs as 5G, but we add “intelligence level” as a new KPI. It refers to the inherent intelligence of communication systems: intelligence of network elements and network architecture, intelligence of connected objects (terminal devices), and information support of intelligent services [52, 53]. A comparison between 5 and 6G in terms of KPIs are shown in Table 5.

4

6G Challenges

• Antennas at higher frequencies are difficult to fabricate [54]. • For THz band, CMOS is saturated and cannot efficiently cope with extreme data rate communication in 6G as it reaches its theoretical limits. Thus, III-V/III-N semiconductor devices such as GaN and InP are promising candidates in terms of power and speed

5

Conclusions

181

Fig. 10 Non-terrestrial communication networks. It may include unmanned aerial vehicle (UAV), high altitude platform (HAP), medium earth orbit (MEO) or geosynchronous earth orbit (GEO) [56]

• Higher frequencies implies complex baseband processing at extreme throughput. • Interoperability of protocols as 6G network will integrate both non-terrestrial (Fig. 10) and terrestrial communication networks [55].

5

Conclusions

In this Chapter, various aspects of 6G communications and network architecture, KPI requirements, key enabling technologies, 6G use-cases are covered. Beyond 5G technologies have the potential to enable fundamentally new applications.

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