Quantitative Analysis of Cognitive Radio and Network Performance [1 ed.] 9781608070992, 9781608070985

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Quantitative Analysis of Cognitive Radio and Network Performance

DISCLAIMER OF WARRANTY The technical descriptions, procedures, and computer programs in this book have been developed with the greatest of care and they have been useful to the author in a broad range of applications; however, they are provided as is, without warranty of any kind. Artech House, Inc. and the authors and editors of the book titled Quantitative Analysis of Cognitive Radio and Network Performance make no warranties, expressed or implied, that the equations, programs, and procedures in this book or its associated software are free of error, or are consistent with any particular standard of merchantability, or will meet your requirements for any particular application. They should not be relied upon for solving a problem whose incorrect solution could result in injury to a person or loss of property. Any use of the programs or procedures in such a manner is at the user’s own risk. The editors, author, and publisher disclaim all liability for direct, incidental, or consequent damages resulting from use of the programs or procedures in this book or the associated software.

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Quantitative Analysis of Cognitive Radio and Network Performance Preston Marshall

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10 9 8 7 6 5 4 3 2 1

Contents Foreword

xv

Preface

xix

Chapter 1

Introduction to Cognitive Radio 1.1 Motivation for Cognitive Radio 1.2 Objectives of This Book 1.3 Summary of Cognitive Radio Conceptual Development 1.4 Cognitive Radio Capability Metrics 1.5 General Assessment Methodology 1.6 A Cognitive Radio Use Case 1.7 Structure of This Book Exercises References

1 1 2 3 6 8 9 10 14 14

Chapter 2

A General Introduction to Radio Design and Operations 2.1 Introduction to Radio Design 2.2 Baseline Superheterodyne Receiver Design 2.2.1 Antenna 2.2.2 Preselector Filter 2.2.3 Low Noise Amplifier (LNA) 2.2.4 Local Oscillator (LO) 2.2.5 Mixer 2.2.6 Intermediate Frequency Filter 2.2.7 Demodulator

15 15 17 18 19 20 20 20 21 21

v

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Chapter 3

Chapter 4

Quantitative Analysis of Cognitive Radio and Network Performance

2.3 2.4

Nontraditional Receiver Design Signal Processing 2.4.1 Modulation 2.4.2 Error Detection and Correction 2.4.3 Architecture and Channel Access 2.5 Impact of Noise on Signal Channels 2.6 Impact of Out-of-Band and Adjacent Channel Signals 2.7 Radio Signal Propagation 2.7.1 Path Loss and Link Margins 2.7.2 Attenuating Effects 2.7.3 Multipath Effects 2.8 Emerging RF Technologies 2.8.1 RF Integrated Circuits (RFIC) 2.8.2 Software-Defined Radio (SDR) Exercises References

21 22 22 24 25 26 27 31 31 34 34 38 38 39 39 40

Conventional and Dynamic Spectrum Management Principles 3.1 Importance of Spectrum Access to Cognitive Radio Concepts 3.2 Conventional Spectrum Management Principles and Practices 3.2.1 Overview 3.2.2 Spectrum Allocations 3.2.3 Frequency Assignment 3.3 Dynamic Spectrum Access Principles 3.4 Other Spectrum Management Considerations 3.4.1 Assumed “Squatter’s Rights” 3.4.2 Out-of-Band Effects 3.5 Emerging DSA Opportunity—TV “White Space” 3.6 DSA’s Role in Cognitive Radio Exercises References

41

42 42 43 46 47 50 50 51 51 52 53 54

A Short Introduction to Cognitive Radio Development 4.1 Overview 4.2 Objective 4.2.1 Spectrum Sharing 4.2.2 Generalized Spectrum Sharing

57 57 59 59 60

41

Contents

Chapter 5

vii

4.2.3 Band-Specific Sharing 4.2.4 Link Operation Enhancement 4.2.5 Flexibility for Adaptive Networking and Upper Layers 4.2.6 User Behavior Analysis 4.3 Implementation 4.3.1 Learning and Genetic Algorithms 4.3.2 Declarative 4.3.3 Knowledge and Trust 4.4 Experimentation 4.4.1 Equipment-Level Experimentation 4.4.2 System-Level Experimentation 4.5 Policy and Standards Infrastructure 4.5.1 Policy and Economics 4.5.2 Standards Development Exercises References

61 63 63 63 64 64 64 65 66 67 67 68 68 69 69 70

General Operating Concept of a Cognitive Radio 5.1 Overview of Cognitive Radio Operation 5.2 Band, Frequency, and Emission Characterization and Selection 5.2.1 Overview 5.2.2 Physical Layer Opportunities 5.2.3 Network Topology Options 5.3 A General Model of Cognitive Radio Decision Making 5.4 Algorithmic Description of Decision Processing Exercises References

77 77 79 79 83 85 87 92 98 99

Chapter 6

Characterizing Spectrum Occupancy of Signaling Bandwidths 6.1 Introduction 6.2 Spectrum Occupancy and Access Characteristics 6.3 Analytic Model of Spectrum Occupancy 6.4 Closed-Form Estimate of Spectrum Occupancy Exercises References

101 101 104 110 116 121 122

Chapter 7

Characterizing High-Energy Environments 7.1 Distribution of High-Energy Signals

123 123

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Quantitative Analysis of Cognitive Radio and Network Performance

7.2 Analytic Treatment of High-Energy Distribution 7.3 Analytic Generation of Front-End Distributions 7.4 Application of Spectrum Distribution Parameters Exercises References Chapter 8

Chapter 9

Synthesizing Distribution Characteristics of Arbitrary Spectrum Environments 8.1 Need for Generalized Environmental Expressions 8.2 Generalized Determinations of Spectrum Occupancy 8.3 Generalized Determinations of High-Energy Spectrum Characteristics 8.4 Example of Spectrum Distribution Synthesis 8.5 Summary Exercises References Analysis of Spectrum Occupancy and False Alarm Rates 9.1 Time-Domain Considerations of Spectrum Occupancy 9.2 The Possibility of False Alarms 9.3 Methods for Reducing the Effect of False Alarm Rate Exercises References

130 135 140 140 141 143 143 145 146 147 147 148 149 151 151 152 153 157 157

Chapter 10 Noise Floor Elevation Due to Intermodulation 10.1 Phenomenology of Front-End Intermodulation 10.2 Analysis of Spectrum Environments 10.3 Front-End Linearity Adaptation Evaluation Metrics 10.3.1 Probability of Front-End Overload 10.3.2 Intermodulation Induced Front-End Noise Elevation Exercises References

159 159 163 168 168

Chapter 11 Front-End Linearity Management Algorithms 11.1 Introduction to Front-End Linearity Management 11.2 Pick Quietest Band First Strategy 11.3 Marginal Noise Impact Strategy 11.4 Front-End Linearity Management Benefits

175 175 176 181 182

170 173 174

Contents

11.5 Reduction in Probability of Front-End Overload 11.6 Reduction of Front-End Noise Floor Elevation 11.7 Front-End Energy Management Conclusions Exercises References Chapter 12

183 187 192 194 194

Selection of Channels to Minimize the Environmental Noise Floor195 12.1 Introduction 195 12.2 Noise Floor Reference Evaluation Metric 199 12.3 Noise Floor Management Algorithms and Methods 200 12.4 Noise Floor Management Benefits 212 Exercises 213 References 213

Chapter 13 Achieving Interference Tolerance in Cognitive Radios 13.1 Interference and Cognitive Radio 13.2 Dynamic Spectrum Access Role in Interference Avoidance and Tolerance 13.3 Spectrum Management Analysis Cases 13.4 Analysis Approach and Assumptions 13.4.1 Communications Range and Receiver Characteristics 13.4.2 Mobility Characteristics 13.4.3 Propagation Characteristics 13.4.4 Operating Characteristics 13.4.5 Analysis Approach 13.4.6 A Quantitative Example Exercises References Chapter 14

ix

Analysis of Interfering and Noninterfering Wireless Operation 14.1 Impact of Spectrum Assignment Methodology 14.2 Interference-Free DSA Operation 14.3 Interference-Tolerant DSA Operation 14.4 Dynamic Spectrum Access Benefits 14.5 Dynamic Spectrum Access Conclusions Exercises References

215 215 221 225 228 229 231 232 234 235 235 237 238 241 241 242 250 261 262 262 263

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Quantitative Analysis of Cognitive Radio and Network Performance

Chapter 15 Minimizing the Spatial Interference Footprint by Cognitive Radio 15.1 Spatial Footprint Management Objectives 15.2 Spectral Footprint Reference Evaluation Metrics 15.3 α-Aware Waveform Selection Principles Exercises References

265 265 271 275 276 277

Chapter 16 Determination of the Density of Cognitive Radio Networks 16.1 DSA and Spectral Footprint Management Impacts on Network Scaling 16.2 Classical Model of MANET Scaling 16.3 DSA-Based Scaling Analysis 16.4 Computation of Density 16.5 DSA Network Scaling Conclusions Exercises References

279

Chapter 17 Network Layer Performance Implications of Cognitive Radio 17.1 Implications on Network-Level Decision Making 17.2 The Open System Interconnection Reference Model 17.3 Dynamic Bandwidth Topology 17.4 Cognitive Radio Enabled Dynamic Networks 17.5 Network Topology 17.6 Quantitative Impacts of Multitransceiver Nodes Exercises References

295 295 296 300 301 304 305 310 311

Chapter 18

313 313 316

Cognitive Radio Application of Content-Based Networking 18.1 General Principles of Content-Based Networking 18.2 DTN as a Metaphor for Content-Based Networks 18.3 Introduction of Content Networking into Cognitive Radio Systems 18.4 Infrastructureless Networking 18.5 Quantitative Effects of Content Management 18.6 Content and Infrastructure Conclusion Exercises References

279 280 281 284 291 292 293

317 319 321 324 324 325

Contents

Chapter 19 Policy and Decision Making in Cognitive Radios 19.1 Implementation Approaches for Cognitive Radios 19.2 Overview of Policy Processing Objectives 19.3 Example Policy Processing Architecture 19.4 Policy Reasoning Technical Issues 19.5 Policy Representation 19.6 First-Order Predicate Calculus Policy Expressions 19.7 Managing the Decision Making of a Cognitive Radio 19.7.1 Addressing Probabilistic Decisions, Belief, and Uncertainty 19.7.2 Decision Theory 19.8 Overhead Costs of Cognitive Radio Implementation 19.8.1 Environmental and Spectrum Sensing Resources 19.8.2 Digital Processing and Storage Requirements 19.8.3 Additional Communications for Awareness 19.9 Summary Exercises References Chapter 20

Performance, Reliability, and Component Trades 20.1 Overview of Cognitive Radio Analysis 20.2 Reduction in Hardware Requirements 20.2.1 Reduced Receive Energy Consumption 20.2.2 Transmit Energy Reduction 20.2.3 Reduction in Spectrum Requirements 20.2.4 Enabling Effective Utilization of Spectrum Markets 20.3 Increased Cognitive Radio Performance 20.3.1 Increase in Operational Availability 20.3.2 Decrease in Noise Floor Probability 20.3.3 Increased Operating Period/Reduced Energy Storage Mass 20.4 Fungibility of Benefits 20.5 Conclusions Exercises References

Chapter 21 Large-Scale System Experiments and Demonstrations 21.1 Overview of Experimentation and Demonstration 21.2 DARPA neXt Generation (XG) Program

xi

327 327 328 329 331 337 338 340 341 343 345 345 346 347 347 349 350 353 353 353 356 357 359 359 360 361 366 367 368 370 371 371 373 373 375

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21.2.1 XG Program Overview 21.2.2 XG Program Field Trials 21.2.3 XG Radio Design 21.3 DARPA Wireless Network after Next (WNaN) 21.3.1 WNaN Objectives 21.3.2 Notional Hardware Concept and Design 21.4 Delay and Disruption Tolerance Networking 21.4.1 DTN as a Vehicle for Content-Based Access 21.5 Conclusion Exercises References

375 375 377 378 378 381 382 383 384 385 385

Chapter 22 Desirable Cognitive Radio Implementation Technology Developments 22.1 Enabling Technology Areas for Cognitive Radio 22.2 Front-End Filters 22.3 RF CMOS 22.4 Policy Enforcement, Decision Making, and Air Interface Processing 22.4.1 Air Interface Processing 22.4.2 Cognitive Radio Decision Processing Exercises References

394 394 395 397 398

Chapter 23 Future Research Needs Towards a Cognitive Radio Ecosystem 23.1 Introduction 23.2 Density and Scaling 23.3 Cognitive Algorithms and Reasoning Expressions 23.4 Assuring Cognitive Radio Stability 23.5 Decision Theory in Cognitive Radio 23.6 Information Theory in Cognitive Radio 23.7 Security 23.8 Conclusions Exercises References

401 401 402 403 404 407 408 409 409 410 411

Appendix A Internet Protocol Networking for Cognitive Radios A.1 Introduction to IP Networking A.2 Basic IP Networking Principles

413 413 414

387 387 388 391

Contents

A.2.1 IP Numbering A.2.2 IP Routing A.3 Impacts on Wireless Devices and Networks A.3.1 Wireless Network Topology A.3.2 Wireless Device Identity A.3.3 Opportunistic and Multihoming Internet Access A.3.4 Naming Services A.3.5 Dynamics A.4 Assumed Cost of Bandwidth and Network Scaling A.5 Summary References

xiii

414 415 416 418 418 420 420 420 420 421 421

Appendix B DVD Contents B.1 Organization of Files B.2 Book Figures B.3 Communications Link Margin Spreadsheet B.4 Spectrum Measurements Data B.4.1 Overview B.4.2 Frequency Domain Files B.4.3 Spectrum Occupancy Statistics B.4.4 Front-End Statistics B.5 MATLAB Routines B.5.1 Monotonic Indices B.5.2 MATLAB Access Routine

423 423 423 425 425 425 427 429 429 434 434 434

List of Acronyms and Abbreviations

437

List of Symbols

443

About the Author

449

Index

451

Foreword Quantitative methods for characterizing the practical value of cognitive radio have been sorely needed for the decade since Chip Maguire and Jens Zander coached me into inventing cognitive radio. Few have done as much to meet that need personally or professionally as Preston Marshall, the author of this important new text. As DARPA program manager of both the neXt Generation (XG) radio program and the Wireless Network after Next (WNaN), Preston has created and managed perhaps the United States’ most influential cognitive radio programs. Early on, Preston created programs to measure the occupancy of the radio spectrum, comparing those measurements against both spectrum allocations on the one hand and against the potential for enhanced spectrum use on the other. His leadership reached beyond DARPA to catalyze the IEEE’s International Symposium on Dynamic Spectrum Access Networks (DySPAN). DySPAN’s demonstrations dramatically illustrated the tremendous new capabilities of radios with the ability to sense spectrum, use white space, and obey spectrum use policies. From the first chapter, Preston focuses on metrics: performance, affordability, and reliability. Commercial, public safety, and military value propositions differ substantially on these metrics. Thus, the behaviors needed of the same cognitive radio for different markets differ substantially, leading to the question of behavioral reliability. If a cognitive radio’s ability to learn allows it to be finessed into inappropriate behaviors, who is responsible? The balance of the book helps the reader learn how to address such key questions. The first section of the book brings readers to a common appreciation for radio design, spectrum management, and dynamic spectrum access (DSA) concepts, including research highlights, concluding in Chapter 5 with a reference architecture. This architecture focuses on the meta-level issues of spectrum sensing, signal in space (also known as waveform) configuration for the environment, and policy conformance to establish a communications baseline that can then be cooperatively optimized by network participants. This leads to a compact yet comprehensive model of physical layer adaptation.

xv

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Quantitative Analysis of Cognitive Radio and Network Performance

Subsequent chapters build on this physical layer adaptation model to quantitatively address spectrum occupancy, power management, signal versus noise, and interference. Closed-form expressions from Chapters 6 and 7 are developed into statistical signal models in Chapter 8, while Chapters 9 and 10 quantify false alarm rate and intermodulation. This middle block of chapters sets up the crucial question of using algorithms to manage intermodulation versus investing in hardware or living with low density to avoid adjacent channel interference (ACI). This idea of using spectrum sensing and transmitter agility to avoid generating ACI allows one to back off of the hardware specifications, increasing ACI measured in isolation while not introducing actual interference to other radios because the cognitive radio can maintain channel isolation of multiple channels. Chapters 11 to 16 then develop these ideas with mathematical models of interference generation, mechanisms to mitigate the interference, and closed-form methods for characterizing cognitive radio footprints with respect to these metrics. The treatment of tradeoffs among performance parameters is founded on quantitative exposition of network layer decision making, management overhead, and policy conformance. Throughout these parts of the text, Preston clearly articulates the key issues and models the critical parameters with well-informed simplifications that render the analytical treatment tractable without inducing significant error, some degree of which is unavoidable with such mathematical models. The overview of large-scale cognitive radio experiments of XG and DTN shows how the mathematical treatments of the prior chapters translate into tangible benefits in the field. This sets up the look to the future of Chapter 22 where technology shortfalls are discussed. Most of these shortfalls are chronic, not yielding to the progress of Moore’s law and so far relatively impervious to new device technologies like meta-materials. Organizing these challenges and shortfalls should stimulate device and circuit technologists to advance these technologies as cognitive radio continues to evolve from research to productization. As Chapter 23 further develops, cognitive radio is not about devices or networks, or about regulation or policy algorithms, but about an interdependent ecosystem among these and other players from academic and governmental research laboratories to markets.

Foreword

xvii

Thus, it is with great enthusiasm, well deserved, that I heartily commend Preston for his latest contribution to the cognitive radio ecosystem and hope you enjoy this book as much as I have. Dr. Joseph Mitola III Vice President, Research Enterprise Distinguished Professor Stevens Institute of Technology Hoboken, New Jersey June 2010

Preface The idea that radios could greatly increase their own capabilities and performance through adaptation has emerged as an exciting field of research over the past decade. During this short period, the field has moved from general concepts, to specific algorithms, to early experimental designs. However, it has not yet transitioned to operational services. One reason for this slow transition is lack of a quantified basis for assessing the comparability of cognitive radio designs, to the equivalent investment in improved circuits, additional spectrum, or other conventional design and fabrication methods. This book is intended to build on prior work in the cognitive radio field, with an objective of providing generalized tools to quantify the benefits of cognitive radio. The metrics for this analysis are the same performance terms that are used for conventional wireless design, and can therefore reflect cognitive radio technology as an alternative to investment in circuitry, energy provision, or spectrum. The linkage of cognitive radio behavior to conventional design decisions provides the mechanism to justify its inclusion in wireless products on the basis of both performance and cost avoidance from circuitry, spectrum acquisition, or energy storage and use. This book also extends current concepts of dynamic spectrum access (DSA) beyond current applications to secondary spectrum sharing. It envisions and quantifies an adaptive ecosystem of wireless devices that inherently tolerate interference, and thereby are able to mutually share spectrum in significantly greater density than through manual or DSA interference avoidance strategies. Unlike other uses of DSA, this strategy provides all users of the spectrum with benefits, and could become a model for new spectrum management regimes based on distributed interference effects mitigation. The book also explores how dramatic changes in networking technology could be enabled by DSA. These include distributed content, name-based networking and content identification, dynamic topologies, and adaptation to social network-driven traffic models.

xix

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Quantitative Analysis of Cognitive Radio and Network Performance

All of these quantifiable benefits point to the transition of cognitive radio technologies to become a core-element in the radio and network designer’s toolkit, whose costs and benefits can be examined and considered in equivalent terms to other aspects of wireless design. This book provides the necessary linkage of the behavioral aspects of cognitive radio, with the quantified aspects of device, link, and network performance and design. To enable the reader to extend this analysis, a DVD containing databases used in the analysis is provided in MATLAB format.

Chapter 1 Introduction to Cognitive Radio 1.1

MOTIVATION FOR COGNITIVE RADIO

The objectives of wireless communications have not changed significantly over time. Even before the advent of long distance RF communications, Nicola Tesla foresaw the objective to be [1]: An inexpensive instrument, not bigger than a watch, will enable its bearer to hear anywhere, on sea or land, music or song, the speech of a political leader, the address of an eminent man of science, or the sermon of an eloquent clergyman, delivered in some other place, however distant. In the same manner, any picture, character, drawing, or print can be transferred from one to another place. For most of the history of wireless communications, the primary enabler for enhanced radio communications utility has been the continual improvement of the implementing technology. Advances in wireless component technology have enabled fundamental advances in communications capability, such as the transition to narrower signaling, amplifying technology (vacuum tubes, transistors), filters (discrete inductors and capacitors, surface acoustic wave, and digital filters), generation of digital and error correcting waveforms, higher frequencies, and lower noise/higher sensitivity receivers. More recently, the focus has shifted to the development and deployment of complex architectures, such as cellular services, where system performance is derived through architectural as well as device performance. Measurement of these advances and assessment of the relative advantages and costs of alternative technologies were relatively straightforward, using measures such as bit error rate (BER), data rate, spectral efficiency, and energy efficiency. 1

2

Quantitative Analysis of Cognitive Radio and Network Performance

These could be compared with the relative costs of incremental technologies in order to optimize cost and performance for a wide variety of applications. This process was possible because the primary measures of effectiveness were directly related to the component technology contained within the radio. Cognitive radio is fundamentally different from this evolution. It creates complex interactions of environment, behavior, performance, reliability, and cost, which are not derivable from conventional radio engineering analysis. By utilizing algorithms and experience-based learning to determine the strategy for delivering a stream of information, cognitive radio is fundamentally different from the preceding technologies. This book provides measurements and analysis techniques to determine the quantitative contribution of cognitive radio in analogous terms to current evaluations of conventional radio and network architectures, and with the same analytic rigor. A quantitative treatment of cognitive radio technology is critical to positioning it equivalently to conventional approaches to achieving radio performance, reliability, and affordability.

1.2

OBJECTIVES OF THIS BOOK

This book has several objectives, which include: 1. Quantitatively describing the environment(s) of a cognitive radio in engineering terms, so that the response and performance of cognitive radio components and algorithms can be readily determined. 2. Providing a methodology to apply spectrum measurements to derive closedform equations of the distribution of environmental characteristics, so that analytic techniques can determine and prove the effectiveness of both cognitive and noncognitive radios operating in the same range of environments. 3. Describing and analyzing the impacts of environmentally informed decisions on the performance, reliability, and affordability of wireless devices, particularly in regimes utilizing the flexibility of dynamic spectrum access (DSA). 4. Describing and analyzing strategies that transition from optimizing link spectrum efficiency to those that maximize overall effectiveness and scalability of dense networks.

Introduction to Cognitive Radio

3

5. Transitioning from DSA as a mechanism to ensure interference-free operation to one that also enables interference-tolerant operation and quantifying the wireless spectrum density that can be achieved by this mode of operation. 6. Providing a qualitative description of the opportunity provided by cognitive radio physical layer adaptation to enable fundamental changes in the operation of the upper layers of wireless networks.

1.3

SUMMARY OF COGNITIVE RADIO CONCEPTUAL DEVELOPMENT

The term cognitive radio was first used by Mitola [2], who described it as: the point at which wireless personal digital assistants (PDAs), and the related networks are sufficiently computationally intelligent about radio resources and related computer-to-computer communications to: (a) detect user communications needs as a function of use context, and (b) to provide radio resources and wireless services most appropriate to those needs. Since Mitola’s work in 2000, a number of different approaches to, and definitions of, cognitive radio have appeared and been discussed. In this book, the working definition of a cognitive radio is a radio that is aware of its environment, and constantly utilizes this awareness to adjust its operating characteristics and behaviors to provide effective performance in a wide range of environments. In contrast, a conventional or noncognitive radio typically has these same decisions made during the design process, or at least in advance of its operation, based on assumptions about the likely future environment (such as dynamic range, spectrum occupancy, path propagation characteristics, user behavior, and network conditions). Some researchers have proposed that it must include learning mechanisms to achieve this threshold; others associate the term with devices that only sense and select spectrum. In this book, we will consider that a particular function is cognitive if it makes its decisions based on a measured or otherwise perceived understanding of the immediate environment. Haykin [3] provides another description of cognitive radio characteristics: 1. To perceive the radio environment (i.e., outside world) by empowering each user’s receiver to sense the surrounding environment continuously.

4

Quantitative Analysis of Cognitive Radio and Network Performance

2. To learn from the environment and adapt to it in response to deviations in the environment. 3. To facilitate communication among multiple users through cooperation in a self-organized manner. 4. To control the communication resources among the multiple users through competition. 5. To create the experience of intention and self-awareness.

Since our definition of cognitive radio is not sensitive to how the logic is implemented, only the first three of Haykin’s points are essential in order for a given operation to be considered cognitive in this treatment. These area attributes we can measure externally to the device or networks. Much of the early enthusiasm for cognitive radio has arisen from interest in developing new mechanisms to manage spectrum access. The rapid growth of cellular and Wi-Fi (IEEE 802.11a, b, g, n) technology stimulated a search for technology to make spectrum available to new applications. The United States Federal Communications Commission (FCC) chartered a Spectrum Policy Task Force [4], which provided some initial recommendations for flexibility in spectrum usage and regulation, and proposed spectrum sharing of both unoccupied frequencies (generally referred to as spectrum “white space”), and as underlay signals, such as ultrawideband (UWB), so long as the aggregate energy did not exceed a specified noise temperature. This process provided considerable legitimacy and promise to the concept of spectrum sharing and dynamic spectrum access (DSA). In turn, this has led to a significant investment in research in the field, broadly addressed under the framework of DSA. A perhaps unintended consequence of the opportunity to share spectrum was that other benefits of cognitive radio have been overshadowed, and much of the literature assumes that a cognitive radio is an unlicensed, secondary user of spectrum, such as cordless phones, Wi-Fi, and UWB technology. The transition of cognitive radio from concept to reality has made considerable progress in the last several years, and has been largely pursued on two paths. The first is the drive to mature, demonstrate, and ultimately deploy DSA systems. A number of commercial and governmental organizations completed prototype implementations and submitted them for varying degrees of technical or operational scrutiny. Much commercial and industry interest has been initially focused on unlicensed access to television white space, such as contemplated by the IEEE 802.22

Introduction to Cognitive Radio

5

Wireless Regional Area Networks (WRAN) standard, while government and academic research often investigated more broad-based spectrum sharing, typically focused on existing licensed applications, such as military and public safety as the initial applications. The deployment of DSA has been proposed as one of the earliest potential applications of cognitive radio. This book will address DSA from two perspectives. The first is the approach described previously: to increase access to needed spectrum. The second is as a mechanism to align the capability of the radio to the stresses of the environment, including adjacent channel and out of band energy, high spectrum and node density, and actual propagation. One way of viewing the cognitive radio construct is that it automates many of the design and management decisions regarding wireless devices. This view closely aligns conventional radio engineering with cognitive radio algorithms. The differences are: 1. These operations need not be performed in advance by human designers, planners, or operators. 2. They do not need to be based on assumed (rather than actual) environments, can exploit less than worst-case conditions, and can avoid situations that could lead to failure. When the concept of cognitive operation is extended to other aspects of the configuration of the radio, and the network, a more general concept arises. A cognitive radio makes as many design decisions as possible, as late as possible, in order that they can reflect maximal use of actual environmental information and usage experience. A good use case for this is in emergency or public safety communications, where unplanned and even inconceivable operational needs are suddenly emergent, resources are inherently strained, infrastructure and expertise to rapidly plan and coordinate communications are difficult or nonexistent, and organizations and nations that have never coordinated communications or operations must suddenly do so. This necessity arises in the absence of the infrastructure they had been assuming in the past! Examples of these communications infrastructure damage scenarios were seen in the recovery operations following Hurricane Katrina in the United States, the Haiti earthquake in 2010 [5, 6], and when mobilizing recovery forces after the 2004 Pacific Rim tsunami [7]. A cognitive radio, in addition to enhanced performance, inherently has the ability to make the operational decisions required to establish communications, even in the absence of the preplanning that is integral to current

6

Quantitative Analysis of Cognitive Radio and Network Performance

communications operations. A cognitive radio should be capable of operating with only the information required to identify the owner, his or her information interests, and assess, decide, and then coordinate all other operational communications and networking decisions.

1.4

COGNITIVE RADIO CAPABILITY METRICS

Throughout this book, the capability implications of cognitive radio will be developed from three perspectives: Performance: What is the increase in the typical performance of the device when cognitive radio functionality is implemented? We can think of this measure as the performance achieved at a stated reliability level (that is, we would expect that for reliability p, the radio would perform below this level only at a (1 − p) rate). Affordability: What is the decrease in resource requirements or cost that would be enabled by the addition of cognitive features, while maintaining equivalent performance and reliability? Reliability: What is the increase in the reliability of the device when cognitive radio functionality is implemented? We can think of this measure as the reliability of achieving a threshold performance level (i.e., we would expect that the threshold performance would be achieved at a probability of p). We can consider that these three considerations form the dimensions of a cube, with axes of performance, affordability, and reliability, which we will refer to as a PAR cube. A PAR cube with several common radio types is depicted in Figure 1.1.1 The three examples shown are: Wi-Fi: High capacity in ideal circumstances, very affordable, but minimal additional reliability mechanisms to ensure operation in stressing environments. Public Safety: Higher cost (medium affordability), higher reliability, and constrained performance. 1

The axes in the figure are notional and relative to depict the design objectives of several radios, and do not represent specific measures.

Introduction to Cognitive Radio

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Military: High cost (low affordability), very high reliability in stressing environments, but typically constrained performance, in order to ensure reliability. The important conclusion is that no single design solution is always the best, and that radio design is inherently a compromise among competing objectives. One vision of cognitive radio is that it uses adaptation to minimize the regrets and makes operating mode decisions that enable the device to most closely approach the 1-1-1 vertex of the PAR cube. This framework is the basis for the discussion in the rest of this book. The question that will be examined throughout this book is: How can cognitive radio maximize the performance, affordability, and reliability characteristics through adaptation to the environment and user, as compared to noncognitive radios? Cognitive radio introduces the complexity that the performance and reliability of the communications link is no longer just a function of the radio capability, but is also a function of the sensing process, the environment being adapted to, and the decision-making algorithms and policies. It is possible that performance and reliability may not always be achieved, due to “errors” introduced by the cognitive radio algorithms, which fail to locate, identify, or execute the decision that best optimizes its objectives. Unique to cognitive radio, is the need to introduce the following additional metric in the analysis process. Behavioral Reliability: What are the decisions to be made by a cognitive radio, and what is the probability of making the optimal/near optimal decision? This is not to say that these four dimensions are independent or orthogonal. Inexpensive consumer communications equipment, such as Wi-Fi cards and family

8

Quantitative Analysis of Cognitive Radio and Network Performance

radio service (FRS) radios address reliability as a secondary objective to cost. Many mission-critical (such as military, public safety, and aviation) communications systems have reliability as the central consideration that drives their cost and constrains their performance objectives. A 2-megabit-per-second radio that works every other day is generally less desirable than one that achieves only 1 megabit per second, but with 100% availability, even though the mean value of throughput is identical. This is apparent when one contemplates rare, but expensive to mitigate, link margin considerations such as high rain rate, extreme fading, or strong adjacent channel occupancy. It is in managing and making environmentally aware decisions in balancing these types of considerations that a cognitive radio obtains much of its benefit. 1.5

GENERAL ASSESSMENT METHODOLOGY

Figure 1.1 introduced the performance, affordability, and reliability trade space. The performance and reliability of a radio link can be analytically characterized, but affordability is inherently different. It is impacted by design costs, volume, availability of commodity parts, and other economic influence rather than strictly dependent on fundamental engineering considerations. Therefore, the early chapters will address affordability implicitly, by considering the effect of component performance levels on performance and reliability. Later chapters will develop analysis and methods for relating changes in the underlying component performance to the cost of wireless devices. Two specific questions will be considered to determine the impact of cognitive radio: 1. With radio performance and reliability held constant, how can hardware component requirements be reduced? 2. With hardware component capability held constant, how can node or network performance or reliability be improved? These are questions that represent specific faces of the PAR cube. The first criterion is the effect of different environmental conditions on the mean or expected value of critical and constraining link performance measures. This measure is appropriate for other than mission-critical applications, or in situations where there are alternative paths, methods, or devices that can be applied, or when the role of the device is such that an occasional reduction in capability would be acceptable. For example, a Wi-Fi link in a 3G/4G cellular handset might not need to be as reliable

Introduction to Cognitive Radio

9

Table 1.1 Metrics of Cognitive Radio Impacts Mean Value of Performance

Reliability of Operation

Improvement in performance

How much more capability would a cognitive radio have, compared to the identical hardware in a noncognitive radio mode?

How much more reliable would a cognitive radio be, compared to the identical hardware in a noncognitive radio mode?

Reduction in required hardware capability

How much can the hardware component performance metrics of a cognitive radio be reduced and still achieve equivalent performance levels to a noncognitive radio?

How much can the hardware component performance metrics of a cognitive radio be reduced and still achieve equivalent reliability levels to a noncognitive radio?

as Wi-Fi in a laptop, since the cellular service can provide services in the event of Wi-Fi failure. The second criterion is the reliability of meeting critical operational performance measures in a range of environmental characteristics. For many communications systems, the driving requirement is not absolute performance, but assured operation, even in highly stressed environments. This measure assesses how proposed adaptation mechanisms can assure operation through mechanisms other than intrinsic hardware performance. A high-level view of the evaluation matrix is shown in Table 1.1. Each chapter will tailor these metrics appropriately.

1.6

A COGNITIVE RADIO USE CASE

In many cases, the discussion of communications and network technology can become abstract, and lose the context of what these systems were intended to accomplish. It is instructive to have a “use case,” which exemplifies the intent of the capability, and how the various technology elements accomplish this intent. The following use case demonstrated the contribution of many of the cognitive radio techniques that will be developed in the rest of this book. We envision an emergency response that involves many of public safety communities converging on a disaster scene. The wireless network performs the following functions and adaptations:

10

Quantitative Analysis of Cognitive Radio and Network Performance

• As vehicles rush to the scene, they opportunistically exploit available Internet connectivity (such as Wi-Fi, 3G, 4G, trunking networks). • As the density of radios exceeds the dedicated frequency allocations, the radios locate spectrum that can be used without interference to themselves or other spectrum users. • As the density further increases, individual links select frequencies to avoid cosite, intermodulation effects. • As backhaul bandwidth is exhausted:

– Content is cached on all devices, so commonly accessed information is recognizable and available on the local network. – Centrally trunked connections, routes, and streaming services are transitioned to direct, peer-to-peer transfers.

• The network topology constantly changes to respond to QOS needs, density, and emerging behavioral patterns. • Resources are managed based on preestablished formal policies that reflect the semantics of the response needs. • Naming, access security, and authentication are flexibly adapted at the scene based on individual needs and information categories. • All Internet backhaul is lost, and the network operates seamlessly using local cached information, direct peer-to-peer transfers, and locally provided naming instead of the now inaccessible Internet domain name system server (DNS) systems. Cognitive radio is not a stand-alone technology, and the techniques to meet this use case are drawn from a range of existing as well as emerging disciplines. Some of these are shown in Figure 1.2. All of these topics and technologies will be explored in the upcoming chapters.

1.7

STRUCTURE OF THIS BOOK

This book is broadly organized into four groups of chapters. This chapter through Chapter 5 are introductory chapters providing an overview of the cognitive radio field. Chapters 6 through 10 characterize spectrum environments and address the

Introduction to Cognitive Radio

;(E&AB(,$"(&*#$%#(/?1(3&$-3B&#,&#B(&3?("(D&#B("(3&

Figure 1.2 Some of the technologies involved in achieving the cognitive radio use case.

operation of cognitive radios within these environmental characterizations. Chapters 11 through 16 address the interaction of cognitive radio nodes within an ecosystem of cognitive and noncognitive wireless nodes, and Chapters 17 through 23 address experimentation, implementing technology, and future research. Chapters 2 and 3 introduce general radio design and spectrum management background. Chapter 2 provides a simplified overview of traditional radio design for the reader without previous exposure to radio and RF communications link design. Chapter 3 introduces a general discussion of spectrum management and DSA. Flexibility in frequency selection is central to most concepts of cognitive radio, and understanding of both the technical and regulatory principles involved is central to much of the material that follows. It is important that the reader recognize that some of the benefits for cognitive radio derive from overcoming regulatory obstacles as much as technical ones. Chapter 4 provides an overview of some research and writings on cognitive radio. The cognitive radio field continues to evolve, and the reader can appreciate the range of approaches and research interests that have been developed in less than a decade. Chapter 5 provides a reference model for the decision structure of

12

Quantitative Analysis of Cognitive Radio and Network Performance

a cognitive radio that includes a wide range of behavior adaptations, and is the reference algorithm structure for the following chapters. Chapters 6 through 8 provide the tools to analyze and synthesize typical spectrum environments. While conventional radios are typically modeled independently of the spectral environment, cognitive radio analysis inherently must reflect specific environments. Performance, reliability, and the permissible cost all are functions of these environments. Chapter 6 provides a model for low energy signal bandwidths. This regime is the one that most impacts the availability of channels within a DSA regime. Chapter 7 characterizes the high energy end of the spectrum environment. This environment often compromises receiver front-end performance through intermodulation and desensitization. These chapters analyze existing spectrum samples, and provide the process for reducing additional spectrum samples to analytic form. Chapters 9 through 12 address performance and reliability implications of the selection of viable frequency choices, based on physical layer considerations. Chapter 9 starts with the choice of frequencies in the simplest implementation of DSA: a fixed threshold for prior frequency occupancy. This chapter also develops analysis of spectrum occupancy false alarm rate, and methods to mitigate the effect of sensing false alarms. Chapter 10 provides an overview of the problems that receivers face with high out-of-band (OOB) and adjacent channel energy, and provides sufficient background for a non-RF engineer to appreciate the issues associated with nonlinear effects on receiver front-end sections. This chapter focuses on the performance of noncognitive radios to provide a baseline for cognitive radio analysis. Chapter 11 provides algorithms and the analysis methods for determining performance of cognitive radios in the same environments, and develops the methodology to perform comparison of cognitive and noncognitive radio designs. Chapter 12 provides analysis of noise floor reduction through selection of low noise channels from a set of acceptable channels. Chapters 13 through 16 address the use of cognitive radio to increase the density of wireless devices. Chapter 13 analyzes the maximum noninterfering wireless density. Chapter 14 provides cognitive radio mechanisms for providing degrees of interference tolerance, as an alternative to ensuring interference-free channels, and develops analysis of the maximum density that can be achieved with interference-tolerant operation. Chapter 15 provides analysis of the density that can be achieved using cognitive radio adaptations that maximize density by managing the interference radius of the radio based on the local propagation conditions, and minimizes the radio’s “spectrum footprint.” Chapter 16 introduces the use of variable topology and communications radius to increase aggregate node density.

Introduction to Cognitive Radio

13

The remaining chapters focus on the technology either available for or required in order to implement cognitive radio functionality, and introduce some future research questions. Chapter 17 examines the opportunity provided by cognitive radio adaptations to make fundamental changes in the operation of the higher layers of the wireless network, and even the fixed network infrastructure. Chapter 18 extends this concept to the positioning of content within the network, by an optimizing process that links the user access behavior at the application layer to the cognitive radio and network decisions that must be made to define the physical layer connectivity. Examining the costs and methods for cognitive radio implementation is important to ensure that the benefits are not outweighed by the costs. Chapter 19 discusses the technology to implement the policy control functions that are implicit in the DSA operation, and potentially in the other optimizing processes. Chapter 20 examines trades across the dimensions of performance, reliability, and component performance and cost. Looking to the implementation of cognitive radios, Chapter 21 provides a summary description of several large-scale cognitive radio experiments that have either been developed or proposed. Chapter 22 discusses emerging component technologies that may become available in the near future, and which may affect the viability of cognitive radio concepts. Finally, Chapter 23 discusses several underaddressed research topics that are important to fully realize the opportunity provided by cognitive radio technology. Supplemental appendices are provided to assist the reader with additional background. Appendix A provides an overview of Internet Protocol (IP) and Mobile Ad-Hoc Networking (MANET), as they relate to cognitive radios and networks. Appendix B describes the contents of the DVD included with this book. This DVD contains representative spectrum environments and MATLAB code to perform some of the exercises and for independent research. Throughout this book, terms will be used consistent with IEEE Standard Definitions and Concepts for Dynamic Spectrum Access: Terminology Relating to Emerging Wireless Networks, System Functionality, and Spectrum Management, IEEE Std. 1900.1-2008, unless otherwise noted [8].

14

References

EXERCISES 1.1

1.2

Draw a flow chart of at least two processes to deconflict conversations during a conference call with numerous participants. Develop the parameters and equations that define the performance and reliability metrics for each method. Define a disaster scenario, and describe how the ultimate cognitive radio should operate during the disaster. Hint: Consult sources, such as Donahoo and Steckler [9] and Leitl [7], for real-world issues and experiences.

1.3

Identify at least three other radio communications systems and describe their design choices in the context of the PAR cube depicted in Figure 1.1. Identify fundamental compromises and the rationale for each of them.

1.4

The PAR cube depicted in Figure 1.1 was described as providing qualitative measures of the three dimensions. Propose a quantified metric for each index, and propose how to position the three radios within the quantified cube. References

[1] W. W. Massie and C. R. Underhill, “The future of the wireless art,” Wireless Telegraphy and Telephony, pp. 67–71, 1908. [2] J. Mitola, “Cognitive radio: An integrated agent architecture for software defined radio,” Dissertation, Doctor of Technology, Royal Institute of Technology (KTH), Sweden, May 2000. [3] S. Haykin, “Fundamental issues in cognitive radio,” in Cognitive Radio Networks, New York: Springer-Verlag, 2007. [4] United States Federal Communications Commission, Spectrum Policy Task Force Report. Docket No. 02-135, Nov. 2002.

ET

[5] C. Rhoads, “Earthquake sets back Haiti’s efforts to improve telecommunications,” Wall Street Journal Online (WSJ.com), Jan. 15, 2010. [6] T. C. Ross, “Radio extends efforts to help Haiti,” Radio World, Jan. 25, 2010. [7] E. Leitl, “Information technology issues during and after Katrina and usefulness of the Internet: How we mobilized and utilized digital communications systems,” Critical Care, Vol. 10(1):110, 2006. [8] IEEE, IEEE Standard Definitions and Concepts for Dynamic Spectrum Access: Terminology Relating to Emerging Wireless Networks, System Functionality, and Spectrum Management, IEEE Std. 1900.1TM -2008, Sept. 26, 2008. [9] M. Donahoo and B. Steckler, “Emergency mobile wireless networks,” IEEE Military Communications Conference (MILCOM), Oct. 17–20, 2005. pp. 2413–2420.

Chapter 2 A General Introduction to Radio Design and Operations 2.1

INTRODUCTION TO RADIO DESIGN

This chapter discusses the general characteristics of the design of radio transmitters and receivers, intended for the reader who is not otherwise experienced in these topics. These designs have evolved over more than a century, but all address a very few core issues. The emphasis on this section is on the design of the receiver, since this component is the fundamental limit of wireless performance and is the device where interference actually occurs. The discussion of this chapter is not intended to be a substitute for a complete and rigorous radio frequency (RF) design course. It is intended to provide the reader with an introduction to those aspects of RF design that are most likely to be impacted by the application of cognitive radio principles, and equally, will constrain the evolution of cognitive radio. This can enable a reader with expertise outside of the RF disciplines to appreciate the impact of cognitive radio and to better understand the quantitative and qualitative arguments presented in this book. One concept that will recur through this book is that of the decibel. A decibel (written as dB) is the relationship between two levels of energy, power, voltage, or current. The reference level of the decibel can be the noise (signal to noise ratio, or SNR), a milliwatt (dBm), a watt (dBW), or any other base. The dB relationship is simply: dB(x, in reference to base) = 10 log10 15

 x  base

(2.1)

16

Quantitative Analysis of Cognitive Radio and Network Performance

Some basic arithmetic in decibels (dB’s) is shown below: dB(a b) = dB(a) + dB(b) a = dB(a) − dB(b) dB b dB(ab ) = b dB(a) Some principles, typical values, and rules of thumb that are useful to memorize include: • Twice the base has a dB value of approximately 3.02.

• A 10-times increase is 10 dB; a 100-times increase is 20 dB. • Losses (ratio less than 1) have negative dB values. • 0 or negative ratios are not expressible.

• Subtraction or addition of the underlying values cannot be expressed.

• Use of decibel units essentially turns multiplication into addition, division into subtraction, and exponentiation into multiplication. As an example, in a set of processing stages, two have gains of 20 times, and one has a loss of 4 times (or a gain of 0.25). The dB value of the net gain in this chain is 17 dB (a factor of 101.7 , or 50), as shown in Table 2.1. A caution in the use of dB is also important. Be careful to differentiate values that are in voltage or current from values that are in power or energy. Since: Power =

Voltage2 Resistance

(2.2)

A process that measures voltage will be measuring a value that is the square root of power. A doubling of voltage yields a four-times, or 6-dB increase in power in a simple circuit (where the effective Resistance does not change). As an example, an analog to digital converter (ADC) in a demodulator responds to voltage. Each higher bit in the output word represents twice the voltage (1,2,4,8, . . . ). However, per (2.2), they represent the square root of the power, or 6 dB per bit.1 A 12-bit ADC therefore can provide 36 dB of voltage or amperage dynamic range, or 72 dB in power. In this book, all dB references will be power or energy relationships, unless explicitly stated otherwise. 1

The change in power is 22 , which is (3 dB)2 . Since exponentiation of a decibel value is performed by multiplying the dB value by the exponent, the power change from doubling the voltage is 6 dB.

A General Introduction to Radio Design and Operations

17

Table 2.1 Example Processing Gain in dB for a Sequence of Gain and Loss Stages Step

2.2

Gain/Loss

1 2 3

Gain Gain Loss

Net

Gain

Ratio

dB

20 10 4

+13 +10 -6 +17

BASELINE SUPERHETERODYNE RECEIVER DESIGN

The most enduring modern radio design has been the superheterodyne receiver. The fundamental concept of this design is that when sinusoidal signals are mixed, they produce products that are the sum and difference of the frequencies of the two signals. In this way, signals can be translated from one frequency to another. In a superheterodyne receiver, this technique is used to move a signal from a frequency that the receiver could not demodulate, or interpret, to one that is more suitable. A block diagram of a general superheterodyne receiver is shown in Figure 2.1. The basic functions of each of these elements are discussed below. Although more and more receiver functionality is transitioning to digital implementation, the bulk of signal manipulation in this baseline design is analog in nature.

%&'(&&)" *+(,-(.(/'0+" $1.'(+" f !"?$"

203"4015(" %67.18(+"" 924%:"

f !"?$"

f !"?$"

?$" @1A(+"

#&'(+6(;1)'(" $+(" $1.'(+" f !"#$"

G(60;=.)'0+"

f !"#$"

%"'0"G"H" -1E&)." *+0/(55"

f !"?$F#$"" 20/)." B5/1..)'0+"

Figure 2.1 Generic superheterodyne receiver block diagram.

C)5(D)&;" -1E&)."

18

Quantitative Analysis of Cognitive Radio and Network Performance

2.2.1

Antenna

Antennas are often one of the most obvious features of wireless devices. Simply stated, they capture a portion of the electromagnetic (EM) field, and convert it to power that is input to the receiver circuit. This book will not describe the theory of operation of antennas, but there are several characteristics of antennas that have significant ramifications for cognitive radio. One is that antennas are themselves RF devices. To be optimally efficient, an antenna typically needs to be an appropriate size: 0.25 of the wavelength (monopole above a ground plane) or 0.5 (dipole) of the wavelength (λ) of the frequency, as determined by (2.3): λ= where:

λ c f

c f

(2.3)

Wavelength (meters, using the units below) Speed of light (approximately 3 × 108 meters/second) Signal frequency (the hertz unit is cycles per second)

Antennas that are not close to their resonant length can be made to function to some extent. However, they are significantly less efficient at coupling energy from space to the radio’s input. A 300-MHz signal has a wavelength of 1m, and a 3-GHz signal has a wavelength of 10 cm. The frequency selectivity of antennas is defined by their Q (quality factor) parameter. For antennas, a low Q is often an advantage, as the antenna is less frequency selective and thus has broader frequency coverage. A second consideration is that an omnidirectional antenna is forced to become smaller at higher frequencies. That means the fixed-wavelength size antennas capture less of the electromagnetic field, as the capture area is proportional to their area. Electrical engineers calculate path loss with a frequency-specific term to reflect this physical constraint. Physicists might consider that energy propagates independent of frequency, but the amount of that energy that is captured is proportional to the area of the antenna. When the frequency doubles, the antenna dimensions are reduced by 50%, the area is reduced to 25%, so the antenna is only 25% (−6 dB) as effective in capturing energy.2 Omnidirectional antennas at higher frequencies are inherently less effective. Increasing frequency results in smaller antennas, at the expense of effectiveness due to the reduction in receive capture area. This is important for cognitive radio for two reasons. 2

This will be apparent when the equation for path loss (2.6) is presented.

A General Introduction to Radio Design and Operations

19

• The frequency bands that have wide blocks of bandwidth available for new applications are typically much higher in frequency, and are therefore disadvantaged in receive antenna effectiveness (as measured by the propagation loss) compared to lower frequencies. • A cognitive radio is anticipated to have considerable range of operating frequencies. It therefore must choose between selecting frequencies with significantly different propagation characteristics. Finally, most practical antennas have directionality. Perfectly sited, vertical and electrically optimal antennas may have very even propagation in all directions, but practical mobile antennas are often quite varied in their horizontal and vertical patterns. And, in realistic personal environments, the proximity to other components of the radio, the blockage of a human body,3 and other effects of less than optimal dimensions (typically due to one antenna having to support many frequencies) mean that even small movements of the antenna can make major changes in the communications reception and transmission. 2.2.2

Preselector Filter

The preselector filter only passes a small set of frequencies, referred to as a bandpass.4 The higher the “Q,” the more selective (narrow band-pass) the filter is. Lowcost devices may have one fixed filter; cell phones typically have one fixed filter for each of the bands in which they operate. High-performance communications equipment will often have a tunable filter that is electrically adjustable through the tuning range. Within a given filter design, more selective filters typically cause more signal loss (attenuation) than less selective ones. The preselector filter serves two purposes. It limits the amount of energy that is passed to the low noise amplifier (LNA). The LNA has limited input energy handling capability (discussed in depth in Chapter 10), so it is important that as many uninteresting signals as possible be removed. Another driver is reduction of image response. Superheterodyne receivers are subject to responding to image signals other than the intended one, so removing these inputs assures that the receiver only responds to the intended frequency. This consideration is further developed in Section 2.2.5. 3 4

Which typically absorbs up to 99% of the transmitted energy that passes through, as it appears similar to a large bag of conductive sea water to RF radiation. For a bandpass design, which is the only filter category that will be considered in this book.

20

2.2.3

Quantitative Analysis of Cognitive Radio and Network Performance

Low Noise Amplifier (LNA)

The low noise amplifier (LNA) amplifies the signal to a level that is sufficient for the subsequent stages not to introduce additional noise. LNAs are typically designed to have very low thermal noise levels, even when it limits the gain and efficiency it can achieve. Ideally, LNAs would be provide just signal gain. Unfortunately, LNAs act not only as an amplifier, but also as a mixer. Every signal entering the LNA mixes with every other signal, and that process creates a large number of signal artifacts, or spurs, that appear to be signals, but are noise. There are many design methods to increase the linearity of the LNA and reduce the generation of these mixing products, but generally, they all involve increasing the energy provided to this stage [1], and the cost of the device. Chapter 10 will further develop and quantify these effects. 2.2.4

Local Oscillator (LO)

The local oscillator (LO) generates signals that, when mixed with the desired signal, will result in a mixing product at the intermediate frequency (IF) (fLO = ±fRF ±fIF ). For example, if the IF was 200 MHz, and the desired signal was 2 GHz, then the LO could generate either a 1.8-GHz or 2.2-GHz signal. From a cognitive radio perspective, we will typically not be involved in the design and operation of the LO. 2.2.5

Mixer

The operation of the mixer has been implied. The mixer produces the product of the LO and LNA signal. Since the LO generates a pure tone, this product is a translation of the input signal to another frequency (fIF = ±fRF ± fLO ). The mixer is also capable of generating intermodulation products, so its linearity is important, and this greatly impacts energy consumption. Even when it is operating linearly, it has the capability of producing spurious responses called images. For example, using the frequencies in the previous example, the receiver would not only receive the desired signal of 2 GHz, but (assuming that the LO was set to 1.8 GHz) it would also respond to a 1.6-GHz signal (since 1.8 GHz minus 1.6 GHz = 200 MHz). This is called an image, and can only be avoided if the preselector provides significant rejection at twice the IF frequency away from the intended receive frequency. If the IF is low compared to the signal, two sets of heterodyne

A General Introduction to Radio Design and Operations

21

conversions must be provided, to minimize the images. It might first translate the 2-GHz signal to 500 MHz, and then convert this to the final IF frequency. 2.2.6

Intermediate Frequency Filter

The intermediate frequency filter further limits the signals entering the demodulation stage to only the desired signal. Typically, this filter has a bandwidth close to the signaling bandwidth. Because most of the signals have been eliminated in the two stages of filtering, there is a much reduced risk of intermodulation effects in this and subsequent stages. 2.2.7

Demodulator

The demodulator restores the information content of the signal. This is the most complex portion of a modern receiver, and interprets the actual modulation of the signal (how information is placed on the carrier signal) as well as methods of error detection and correction (EDAC). In general terms, information can be encoded as amplitude, phase, and frequency. Every modulation approach has at least one environment in which it excels, and a community of engineers who are its advocate. Popular modulation families use multiple of these dimensions, such as quadrature amplitude modulation (QAM), which uses both phase and amplitude together. The implications for cognitive radio will be discussed in more detail shortly. This block generally includes conversion from the baseband signal to digital representation, signal processing to identify the symbols being transmitted, and some degree of EDAC processing. Most modern radios also have some degree of signal framing (organizing the start and stop point of blocks, and generally also provide some control over the MAC layer operation).

2.3

NONTRADITIONAL RECEIVER DESIGN

Although the superheterodyne receiver has been the workhorse in the field for decades, new designs that leverage more extensive digital technology have emerged. In general, these are not better in terms of performance, but they are often simpler and can be manufactured using more monolithic fabrication, which greatly reduces their cost. From a cognitive radio perspective, these designs are important, because

22

Quantitative Analysis of Cognitive Radio and Network Performance

if cognitive radio can enhance their performance to the point where their performance is competitive with conventional designs, then there is a significant opportunity to accelerate their deployment, and with them, a much wider application of wireless devices. One of the emerging designs is the direct-conversion, or zero-IF receiver. This design has the LO frequency equal to the transmitter frequency, so the result of the mixing process is the actual baseband modulation. This design had a number of practical issues when implemented using conventional discrete components, but is well suited to the emerging highly integrated complementary metallic-oxide silicon (CMOS) RF integrated circuits (RFIC). One of the drawbacks of this design is that the LO must operate on the receive signal frequency, and any energy from this source can “jam” the desired signal. This limits the amount of LO mixing energy, which limits the dynamic range. Additionally, modulation products close to the transmit frequency appear as direct current (DC) at the output, or, conversely, any DC bias in the circuit appears as modulation content. However, the attractiveness of this design from a cost perspective makes it likely that there will be increased application of this design, at least in the low performance end of the wireless practice.

2.4

SIGNAL PROCESSING

Signal processing is used to convert the time, frequency, and phase modulated signal into the original information. The following discussion will limit itself to digital, but the references include techniques for modulation of analog signals. The following discussion is not intended to be comprehensive, and is focused on those aspects of waveform consideration and selection that are of particular interest to the cognitive radio designer or analyst. There are different aspects and organization of waveform operation, but this discussion will partition them into three functions: the modulation, the error detection and correction (EDAC), and the method of operating and controlling access to the channel. 2.4.1

Modulation

The modulation is based on the fundamental information theory laid out by the Shannon-Hartley theorem [2]. This shows the limit of a channel with finite bandwidth, and a finite amount of signal to noise level. The relationship of the channel information capacity, the channel bandwidth, and the signal and noise power is

A General Introduction to Radio Design and Operations

given by:

where:

23

  S C = B log2 1 + N

(2.4)

Channel capacity (in bits/second) Bandwidth (in hertz) Total signal energy Total noise or interference energy (in same units as S term)

C B S N

Although it is appealing to think that spectral utilization can be improved by using more bits per hertz, this equation for S  N reduces to:   S C Bits = = 3.3 log10 (2.5) Hertz B N The bits per hertz increase linearly with the log of the signal power (given fixed noise). Each time the signal power is doubled, it achieves the same increase in bits/hertz, so the last single bit increase is essentially as expensive in energy as all of the previous bits combined. This effect is shown in Figure 2.2, which illustrates the theoretical bits per hertz curve derived from (2.4). 7

Bits/Hertz

6 5 4 3 2 1

0

10

20

30

40

50

60

70

80

90

100

Signal to Noise Ratio

Figure 2.2 Theoretical modulation performance.

Common modulations have a range of bits per hertz. Some are shown in Table 2.2 for a bit error rate (BER) of 10−6 .

24

Quantitative Analysis of Cognitive Radio and Network Performance

Table 2.2 Characteristics of Commonly Applied Modulation Techniques Modulation Family Quadrature Phase Shift Keying (QPSK) Differential Quadrature Phase Shift Keying (DQPSK) Minimum Shift Keying (MSK) Binary Phase Shift Keying (BPSK) Differential Binary Phase Shift Keying (DBPSK) Orthogonal Frequency Shift Keying (OFSK)

2.4.2

Hertz/ bit

Required Eb N0

1.0 1.0 1.5 2.0 2.0 2.0

10.6 dB 12.8 dB 10.6 dB 11.0 dB 11.2 dB 14.0 dB

Error Detection and Correction

The previous section introduced the concept of energy per bit. Interestingly, the “bit” referred to is not the bit as it is transmitted, but the bit that is provided to the upper layer of the radio. The waveform designer has the choice of placing all of the energy for a bit behind one transmitted bit, or can be divided among multiple bits and combined at the receiver. The advantage of this latter approach is that the receiver can use this information to both detect and correct errors. The simplest form of this could be that every bit is simply sent twice. If both bits are the same, the receiver assumes the information is good. If they differ, it can detect that one of them is wrong, but not correct the error, since it cannot tell which is in error. If both are actually wrong, it can not detect this, and would improperly “pass” the incorrect information. We can extend this concept to add two extra copies of the bit, along with the original single bit. That way, it could detect if any of the bits were wrong, and could correct any single error, but at the price of transmitting three times as many bits on the channel. A more reasonable approach would be to add two bits to an 8-bit byte. That would add 25% to the channel, and would correct one error out of each set of 10 transmitted bits, and detect any two bit errors.5 One measure of the quantity of the codes generated by this process is referred to as the Hamming distance. This is the minimum difference between any two encoded symbols. The 5

Note that the method described here is for purposes of demonstrating the error detection and correction process, and is not as effective as many of the techniques in the literature. Actual selection of these methods is quite complex, and requires a fundamental understanding of the character of the specific channel effects to be mitigated, in order to be optimized for each application.

A General Introduction to Radio Design and Operations

25

more different (distant) these code words are, the more errors can be detected and corrected, generally at the cost of increasing the overhead. The above simplistic formulation leads to the general characterization of EDAC schemes. They detect and correct m out of n bits. The rate of such a code is defined as the number of information
bits over
the number of
transmitted bits, so our three examples would be rate 21 , rate 13 , and rate 45 , respectively. There has been extensive research and engineering performed to determine the optimal methods to distribute and combine these bits, but this is beyond the scope of this book. 2.4.3

Architecture and Channel Access

Although not directly related to modulation, there is considerable diversity in the methods used to manage channel access and contention. Some systems may elect to apply more than one of these techniques. Some of the most common channel access methods include: Spread Spectrum: Spread spectrum operates essentially by multiplying each information sequence by a spreading code. Thus, each symbol expands in bandwidth by the number of bits in the spreading code. In exchange, a receiver can listen to a single frequency and separate the various transmissions by filtering, using the original spreading code. Links can share a common frequency, and are defined by their spreading code instead of their frequency. This technology is effective when the range between units can be controlled so that a node does not have to receive unwanted but powerful links at the same time it is receiving low strength signals.6 This technology is the basis for code division multiple access (CDMA) used in the links of some FDD (below) cellular systems. Other variants are direct sequence spread spectrum (DSSS) and pseudo-noise spread spectrum (PNSS). Some implementations of ultrawideband (UWB) signaling are also a form of spread spectrum. Time Division Duplex (TDD): Time division duplex (TDD) separates transmissions in time. Nodes transmit and receive on the same frequency, and through the access protocol, contend for access to a shared channel. The protocol avoids multiple nodes transmitting at the same time and having the signals collide at the receiver. IEEE 802.11 (or Wi-Fi) is an example of a TDD system. TDD is well suited to peer-to-peer (P2P) systems, since all nodes operate symmetrically and can hear all other nodes. 6

This situation is referred to as a “near/far” condition.

26

Quantitative Analysis of Cognitive Radio and Network Performance

Frequency Division Duplex (FDD): Frequency division duplex (FDD) systems use separate uplink and downlink frequencies. For example, in cellular systems, base stations transmit on one frequency and handsets on another to avoid interference and overload effects. These paired frequencies are far enough apart that the receiver is not subject to overload from adjacent units. Handsets cannot hear the frequency that they (or their neighbors) transmit on, so this approach is generally limited to hub and spoke systems such as cellular or trunking systems. In most applications, the division of uplink and downlink frequencies is fixed, so the spectrum cannot be dynamically rebalanced. This makes this technique inappropriate for many of the approaches described in the later chapters. Slotted: Slotted systems assign each node a specific slot to transmit in. Slots are a block in time that repeats cyclically. The Global System for Mobile Communications (GSM) cellular standard is slotted, as cell phones are assigned a specific slot when a call is established.

2.5

IMPACT OF NOISE ON SIGNAL CHANNELS

The modulation schemes shown in Figure 2.2 are shown for near perfect reception of the signal. Figure 2.3 illustrates the sensitivity of a typical modulation, binary phase shift keying (BPSK) to noise [3]. An important aspect of all wireless communications is the degree of correlation of the noise and other link effects. Shannon developed his theoretical analysis on the assumption that noise was Gaussian. Gaussian means that each noise “event” is independent. This would imply that the state of the channel at one instant has no effect on, and provides no information about, the likely state of the channel at the another time, position, or frequency. Many of the interesting sources of path loss and noise are highly correlated. If there is a building that is blocking the signal path at one instant, it is probably still blocking the path at the next instant. If there is interference, it is probably still there a nanosecond later, and so forth. This has a profound impact on wireless systems. Satellite, wired, or fiber-optic link designers can address the mean, or average, case and assume that the environment is distributed normally around it. In wireless we must deal with the fact that the conditions that cause issues with one symbol are also highly likely to disrupt the next symbol or packet. While link designers generally have their strongest tools in Gaussian environments, cognitive radio has the advantage that it can learn and strategize how to address correlated

A General Introduction to Radio Design and Operations

27

−1

Bit Error Rate

10

−2

10

−3

10

−4

10

−5

10

−2

0

2

4

Eb/No (dB)

6

8

10

Figure 2.3 Theoretical bit error rate performance for BPSK.

problems because their very persistence can be leveraged. In future chapters these techniques will be developed, but they all involve either leveraging the persistence of correlated effects to better optimize operation, or using alternative modes of operation (such as modulation, waveforms, frequency selection) to “Gaussianize” the correlated effects, such as noise, so that the radio can operate in an effectively Gaussian environment.

2.6

IMPACT OF OUT-OF-BAND AND ADJACENT CHANNEL SIGNALS

A simple model of RF communications could lead one to the conclusion that each frequency operated independently of every other frequency, and that the process of examining spectrum was separable, that each individual frequency was an independent decision. This section addresses the practical effects and constraints introduced by realistic device operating characteristics and performance. One of the most important effects is the impact of total input energy on the performance of the receiver front-end. Advocates of DSA have often assumed that any available “white space” would be usable for communications. In fact, white space next to a strong emitter may be unexploitable regardless of policy.

28

Quantitative Analysis of Cognitive Radio and Network Performance

In fact, the drive for addressing these concerns is not specific to DSA systems. In the United States, a lengthy and controversial regulatory proceeding was initiated to resolve interference between the base stations of a cellular service provider, NEXTEL, and numerous local public safety systems. In the end, the U.S. regulator (the FCC) elected to relocate the systems through a mix of spectrum offerings and cellular provider contributions to public safety frequency relocation [4]. These systems did not overlap in frequency usage, so this was not a spectrum management failure, as typically defined. But the placement of high power cellular base stations did have a very significant impact on the performance of the public safety radio systems due to the very high energy level in adjacent frequency bands. Similar anecdotal experience is often referred to as “cosite interference,” as it is often the product of placement of a receiver in close proximity to a relatively strong emitter within the frequency response range of the receiver’s front-end. One of the contentions of this book is that front-end overload, due to energy in adjacent signal channels, is a common and generally underrecognized operational experience. This phenomenon could become a fundamental constraint on wireless networking as spectrum assignments become denser and as technologies such as DSA become more prevalent. Finding a solution is therefore imperative if wireless density is to be increased, with reasonable constraints on receiver front-end linearity, cost, and energy consumption. As RF devices become integrated, the inherent limitations of CMOS processes may result in future highly integrated RFICs being even more sensitive to the effects of constrained front-end linearity. A representative quantitative example of this situation is shown in Figure 2.4. Figure 2.4(a) depicts the input spectral distribution of eight (unequally spaced) signals as a typical input to an LNA. In this case, the total energy is just below the IIP3 of the LNA. The input signals have separations ranging from 40 to 100 MHz. Figure 2.4(b) illustrates the spectral distribution of the same signal set after amplification by the receiver LNA. The LNA processing these input signals generates intermodulation products, or artifacts, essentially every 10 MHz throughout the octave range and beyond. A denser and more complex signal mix would create correspondingly denser and even more complex sets of intermodulation products. The effects of receiver front-end energy loading are currently a significant factor in the performance of conventional radio devices that inhabit densely populated (RF devices) environments with either discrete high power emitters or aggregations of emitters collectively resulting in high energy levels. Current manual planning processes have generally introduced an implicit control over the environment to which wireless devices are subjected. Even with effective manual planning and high

A General Introduction to Radio Design and Operations

29

LNA Input Spectrum

0 -10

(a)

dBm

-20 -30 -40 -50 -60 -70

600

800

1000

1200

1400

1600

1800

MHz

LNA Output Spectrum

0 -10

(b)

dBm

-20 -30 -40 -50 -60 -70

600

800

1000

1200

1400

1600

1800

MHz

Figure 2.4 (a, b)

Effect of low noise amplifier distortion on spectral distribution.

performance RF equipment, there are numerous instances of interference that have been caused by nonlinearity within RF receivers. This problem is important to the DSA and cognitive radio community for three reasons: 1. One of the primary benefits of DSA is operation in bands that currently are dedicated to other uses. DSA radios therefore will be subject to a much greater range of environments than band-specific and frequency-managed products are in conventional spectrum practice. 2. The future deployment of DSA and cognitive radios will make this situation become more stressing as spectrum density is increased by technologies such as DSA.

30

Quantitative Analysis of Cognitive Radio and Network Performance

3. Provision of high front-end performance is a fundamental cost and energy driver for wireless devices and is a significant operational impediment. Adaptations that minimize the effects of intermodulation enable significant reductions in required IIP3 performance and increase reliability beyond what is possible through high power and high cost circuit approaches. As a minimum, overload will reduce performance by raising the effective noise floor, and often may preclude operation of the device at any capacity level. It has been suggested that a cognitive radio can adapt the use of spectrum to avoid situations in which the front-end will be overloaded or desensitized (such as by AGC behavior). A number of strategies have been employed to mitigate the receiver desensitization and nonlinear effects of the radio environment. One commonly applied approach is automatic gain control (AGC). This adjusts the receiver gain in one or more stages to remain within the dynamic range of the circuit. AGC to address a stronger than necessary desired signal has negligible effect on link operation, as it is only required in the situation in which excess signal is present. The receiver dynamic range is generally far in excess of the flat BER portion of the Shannon curve (Eb /N0 ), so reducing the front-end gain is acceptable, when the energy controlling the AGC level is the desired signal. However, if AGC operation is required due to the energy of an adjacent in-band signal, then the loss of gain due to AGC action raises the effective receiver noise floor and therefore reduces link performance. AGC is an effective technique for in-channel energy, but compromises link performance if required to reflect energy of adjacent signals.7 Note that a radio may have to address more than the environmental effects; duplex operation may generate intermodulation effects that fall into the receive bandwidth, particularly if multiple transceivers are in use. It is assumed that the device is aware of its channel usage, and therefore can reflect any additional constraints into its decision process. If the device is operating with static frequency assignments, there is little effective mitigation of this front-end overload condition, other than high performance receiver front-end performance. On the other hand, if the device were permitted to select its own frequencies, then implementing algorithms to select frequencies without overload conditions is a reasonable solution. 7

When AGC response causes the demodulator to lose performance in demodulating the signal that would have been acceptably demodulated except for the impact of the adjacent signal, it is referred to as “desensitization.” Desensitization from the intended signal itself is not a concern, since the receiver would be operating with a high SNR.

A General Introduction to Radio Design and Operations

2.7

31

RADIO SIGNAL PROPAGATION

2.7.1

Path Loss and Link Margins

Digital radio communication is dictated by the necessity to maintain the necessary signal to noise ratio, which ensures the necessary Eb /N0 is maintained. The process to verify this condition is instructive. This section will demonstrate the methodology to compute link margin for basic communications links. The sample problem to examine is: A 2.4-GHz communications link will operate over a range of 50m, using QPSK modulation to communicate a raw data stream of 1 Mb/sec.8 Assume that there is up to 20 dB of multipath and attenuative effects, and that the antennas are each within 1 dB of isotropic (omnidirectional) radiation. Also, the inexpensive receiver has front-end noise that is 15 dB above the thermal noise level. The planned power is 10 dBm. Will this work, and if so, by how much could the transmit power be reduced and still meet the link conditions? Basic link margin worksheets are shown in Table 2.3 and Table 2.4. Table 2.3 Example Transmitter Link Margin Parameter Transmit power Transmit antenna gain Path loss Fade margin Receiver antenna gain Power at receiver

Symbol

Value

Units

PT GA PL M

10.0 −1.0 −74.0 −20.0 −1.0 −86.0

dBm dB dB dB dB dBm

PRCV

Now, we will consider each of these entries. Using decibels (dB) makes the computation less complex, since we can add and subtract them rather than multiply by very large and small numbers. The initial transmit power is given. A power of 10 dBm corresponds to 0.01 watts, or 101 milliwatts, which is the dBm unit. The anticipated antenna gain is negative. The loss of 1 dB corresponds to a reasonably small portion of the power 8

Mb is megabits while MB is megabytes.

32

Quantitative Analysis of Cognitive Radio and Network Performance

being lost in the connection to the antenna or the antenna itself. This is quite good for an omnidirectional device. The path loss represents two phenomena; the loss of signal strength with distance, and the amount of power which is intercepted by the antenna. Every time the wavelength is reduced, the capture area of the antenna is also reduced. If the frequency is doubled, the capture area is reduced by three quarters, and there is four times (6 dB) more path loss. Path loss is computed by (2.6) using the λ determined from (2.3). Note that this computation assumes that strength of the transmission is reduced by the square of the range, as would be true in “free space” propagation conditions.9 Later chapters will consider the opportunity for cognitive radio when this assumption is not appropriate, such as when communications paths are close to the ground. Path Loss (in dB) = PL = 20 log10 where:

λ r

4π r λ

(2.6)

Wavelength (in meters) Link distance (in meters)

In the example, we will transmit at 2.4 GHz, which has a wavelength (λ) of 0.125m. The path loss for a range of 50m is therefore 74.0 dB, which we consider in the link margin as a negative value, since it is a loss. The fade margin is a value determined from experience in the environment, and represents any phenomena that would reduce the signal level. Depending on the application, one might introduce margin considerations for factors such as ice, rain, multipath (discussed next), and building attenuation. An allowance of 20 dB is reasonable for a terrestrial mobile radio. The National Institute of Standards and Technology provides some experimental measurements and analysis of attenuation of a range of building materials and frequencies [5]. At the end of this process we have determined the minimum power that could be transmitted to the front-end of the receiver. Calculating the receiver operation is a bit more complex. First, we must determine the internal noise level of the receiver. The thermal noise is expressed as the energy that would be generated by a “perfect black body” expressed in Kelvin. Boltzmann’s constant provides the energy per hertz (spectral density) of the noise. This is then multiplied by the receiver bandwidth to determine the amount of energy that will be present in the demodulator. For a device at room temperature, 290K is 9

Free space conditions include space communications, or when the propagation path is far from reflecting or absorbing objects, such as the ground, structures, atmospheric effects, etc.

A General Introduction to Radio Design and Operations

33

Table 2.4 Example Receiver Link Margin Parameter

Symbol

Thermal noise Additional receiver noise Receiver noise floor Required signal to noise Estimated receiver sensitivity

kT B NF

Estimated power to receiver Actual link margin Minimum possible power

PRCV

SNR

Value

Units

−114.0 15.0 −99.0 10.6 −88.4

dBm dB dBm dB dBm

−86.0 2.4 7.6

dBm dB dBm

a reasonable estimate. Noise = kT b0 where:

T k b0

(2.7)

Noise temperature of the receiver Boltzmann’s constant (1.38×10−23 J/K) Signal bandwidth

Substituting these values yields a thermal energy of −114-dBm power across the 1-MHz bandwidth.10 Adding these factors provides the total noise power being generated internally to the receiver, within the bandwidth of the signal. This provides the N0 term for our demodulator. In this case it is −99 dBm. Because we are considering a poor receiver in this example, additional receiver noise of 15 dB is added to the noise floor to account for internal noise. This yields an effective noise floor of −99 dBm. We now need to determine the needed signal. The required ratio of energy per bit over noise is known for the commonly applied modulations, and is shown in Table 2.2. We will use a QPSK modulation, and target a 10−6 BER, which yields a required Eb /N0 of around 9.5 dB. This value, plus the relationship of the bit rate to the noise bandwidth, provides the required signal to noise ratio (SNR) for successful demodulation. 10 Note that Boltzmann’s constant is typically expressed as joules and the bandwidth in time units of seconds. Therefore the use of this k value yields units of joules/second or watts. To convert to dBm, the value is multiplied by 103 , or alternatively, 30 dB is added.

34

Quantitative Analysis of Cognitive Radio and Network Performance

SNR = where:

R b0 Eb N0

Eb R N0 b0

(2.8)

Data rate of the signal Occupied bandwidth Required signal energy over noise for the selected modulation

If the ratio between the available energy and the required energy is more than one (0 dB), the link will operate (close, in communications terminology), even when the fade condition is at its maximum value. In this example case, there is a 2+ dB surplus of energy. A 7.6-dBm transmit power could have been used and still just meet the link closure minimum. An Excel spreadsheet is included in the accompanying DVD for experimentation with different link designs. This process illuminates several of the opportunities for cognitive radio. The 15-dB fade margin is probably not needed most of the time, so a cognitive radio could reclaim this capacity. Similarly, instead of letting the link fail, it has the option of reducing data rate, bandwidth, or required Eb /N0 by varying the modulation. In a cognitive radio, the link margin can be an interactive decision rather than a set of rules that are cast in stone. 2.7.2

Attenuating Effects

The simplest link path disruption is attenuation. The path loss calculated earlier was for a path with minimal matter. Anything in the way of RF absorbs some of the energy. Building material is very effective shielding at VHF and above. Diatomic oxygen rapidly absorbs almost all energy at 60 GHz. Atmospheric ice and rain rate are major causes of outage, as anyone watching a satellite TV signal during a heavy rainstorm can attest. 2.7.3

Multipath Effects

Multipath is a more complex effect. In multipath, the signal is received from two different length paths, as shown in the simple case depicted in Figure 2.5. Depending on the difference in path length and frequency, the multiple signals can either reinforce themselves if they are in phase, or they can be destructive if they

A General Introduction to Radio Design and Operations

35

are out of phase. If the signals are constructive, the increase is 3 dB; if destructive, the signal can be essentially destroyed.

!"#"$%&'()*+,-$"( !"#

"$0 "3( 1-0 2(

./+"$0(1-02( Figure 2.5 Multipath propagation channel effects.

These effects can change quite rapidly in frequency, time, and space. When there is movement between the source, receiver, and reflecting surface, the path changes rapidly from being constructively interfering to becoming destructively interfering, and back. This produces a “fast fade” in the signal. Figure 2.6 shows a 6-cm displacement along a path between two reflectors. Note how rapidly the channel fades from usable to unusable. A top-down view of the center 6 cm by 6 cm region of this environment is shown in Figure 2.7. Note that in the areas where the signal is dominated by one of the reflectors, the effects are reduced. Figure 2.6 is a 45-degree angle traveling down the diagonal of this graph. A similar effect occurs in frequency. The interference effect (constructive or destructive) is a function of the difference in path length, modulus the wavelength. The wavelength varies by frequency, so different frequencies within a signal may be in different interference states (constructive and destructive interference). A wideband signal is very likely to have some of its energy in a faded frequency and some in a reinforcing frequency. This distorts the signal in terms of its total energy, but more importantly, it distorts its spectral character, which is equivalent to distorting it in time. Figure 2.8 shows the effect on the channel across a range of frequencies. Not only does the channel response vary by frequency, the response varies by position, as changes in the geometry cause different frequencies to be

36

Quantitative Analysis of Cognitive Radio and Network Performance

2 1

Signal Strength (dB)

0 −1 −2 −3 −4 −5 −6 0

1

2

3 4 Distance (cm)

5

6

7

Figure 2.6 Multipath propagation fast fade.

reinforced and others to be canceled, using the same channel as shown previously in Figure 2.6. This effect is significant. Typically, receivers must “equalize” the frequency domain to make the channel appear “flat” in the frequency domain. This means estimating the channel response shown in Figure 2.8, determining the inverse transform, applying that to signals in the time domain, and then processing. This process is referred to as “equalization”. From a cognitive radio perspective, these effects are important, because they complicate the radio’s ability to understand its environment. What is known about propagation at one time, about one frequency and one position, may not be applicable to an adjacent frequency, time, or position. Multipath conditions also can create additional effects if the path length difference is quite long compared to the time it takes to transmit a symbol. Consider

A General Introduction to Radio Design and Operations

37

−1

−2

−3

−4

−5

−6

−7

−8

−9

Figure 2.7 Multipath propagation map showing fading (in dB).

5

0

Signal Strength (dB)

!5

!10

!15

!20

!25

!30

!35 700

720

740

760

780

800 820 Frequency (MHz)

840

860

Figure 2.8 Multipath frequency selective channel.

880

900

38

Quantitative Analysis of Cognitive Radio and Network Performance

a 10-Mb/second link with a modulation of 1-bit symbols. The symbol rate is therefore 1×107 . Since light only travels 3×108 meters/second, a path length difference of just 30 meters would have one symbol arrive along the longer path after the same symbol had arrived on the shorter path. It would arrive along with the second symbol from the shortest path. This is called intersymbol interference (ISI) because the additive effect is at the symbol time scale, not the RF carrier. In this case, the concept of addition or cancellation of energy is inapplicable, since the information arriving along the two paths is different. One of the solutions to this problem is to send many slow channels in parallel. Orthogonal frequency division multiplexing (OFDM) selects carriers that are exactly spaced to not have any overlap, and transmits a large number of individual carriers (perhaps 1,000), each containing a proportional amount of the bit stream. This slows the symbol time down by 103 , so the difference in the multipath delay would have to be 30 km to have it arrive completely in an adjacent symbol. This approach is highly effective in addressing multipath-induced ISI, but at the cost of making the signal much harder to amplify, since the level of the signal it generates can vary as each of the subcarriers takes on different amplitudes. This is characterized as the peak to average ratio (PAR).

2.8 2.8.1

EMERGING RF TECHNOLOGIES RF Integrated Circuits (RFIC)

One of the reasons wireless devices have become a low-cost commodity item is that it is now possible to fabricate reasonably performing RF circuits by the same semiconductor fabrication processes that produce the high density digital circuits, particularly CMOS, and integrate them directly with the digital processing circuits on a single chip. There are a number of advantages to this approach, primarily reduction in the number of parts in the receiver, and direct connections between digital and analog circuits. Since digital circuits are only required to reliably distinguish between two states (those associated with 0 and 1), digital designs tend to have lower dynamic range and linearity. Their performance as RF circuits is typically much less capable than the technologies that are utilized in discrete component fabrication. The importance of this will be analyzed in Chapters 10 and 11. Typical RFICs are highly affordable, but of limited application flexibility.

A General Introduction to Radio Design and Operations

2.8.2

39

Software-Defined Radio (SDR)

Software-defined radio (SDR) is a technology often associated with cognitive radio, due to its strong dependence on software behaviors and the opportunity it presents for highly flexible modes of operation. In fact, a cognitive radio could be built with a set of fixed-mode (waveform, protocols, frequency bands, and so forth) transceivers, but typical concepts make use of the software-defined modulation and operation of an SDR to provide the range of modes and operating characteristics that define the options for a cognitive radio. SDR offers a number of advantages, but the most critical one from the perspective of a cognitive radio is the ability to adjust the operating modes along a continuum, and to introduce additional modes from experience. The algorithms described in this book assume considerable flexibility in choice of operating mode, and that flexibility can be provided through extensive, but discrete operating modes, or through the flexibility provided by SDR [6]. A good overview of the interaction of physics, physical devices, and information theory is provided by Neil Gershenfeld [7]. EXERCISES 2.1

Ability to do arithmetic in dBs without a calculator is very convenient for RF design. Determine the dB values of the following power ratios to two significant digits. DO NOT USE A CALCULATOR! 8, 20, 40, 200, 0.25, 0.2, 0.0004.

2.2

Using the example in the DVD, and shown in Table 2.3 and Table 2.4, determine how the minimum transmit power would change if other ISM frequencies were used, including 900 MHz and 5.8 GHz.

2.3

Using the example in the DVD, and shown in Table 2.3 and Table 2.4, determine how the minimum transmit power would change if the other modulations shown in Table 2.2 were used instead. (Hint: Both bandwidth and required signal levels change with this modulation.)

2.4

Research the typical design of a 2.4-GHz 802.11g Wi-Fi card. Compute this at range of distances in order to determine the maximum data rate that can be supported in an ideal link at each distance, out to where the link would fall below 1 megabit/second.

40

References

2.5

Using the analysis from the above problem, locate an access point in an area without any other interference, and plot the connection speed to the access point from several laptops. Compare these speeds to the predicted maximum rates.

2.6

Using a laptop and a Wi-Fi access point, experiment with inserting a variety of material (such as wallboard, tin foil, or humans) and measure the effect on the connection speed. Initially position the laptop at the farthest distance from the access point that will support a full-speed connection. (Note: Do not use 802.11n modes, and avoid reflecting environments.)

2.7

Using Table 2.2 and (2.5), plot the theoretical bound and the currently applied waveforms. Compare the current practice to the theoretical bound.

2.8

You are a consultant to a venture capital firm. They have sent you a funding proposal, but it got caught in your spam filter. The inventor claims to have a modulation replacement for QAM that uses 20 times less energy than the current implementations. It would be revolutionary. You retrieve the message the minute they are to make a decision. You call them, but your cell phone battery has only 30 seconds left. What do you tell them? References

[1] U. L. Rhode and D. P. Newkirk, RF/Microwave Circuit Design for Wireless Applications. Wiley & Sons, 2000.

John

[2] C. E. Shannon, “A mathematical theory of communication,” Bell System Technical Journal, pp. 79– 423 and 623–656, July and Oct. 1948. [3] J. Proakis and M. Salehi, Digital Communications, 5th ed.

McGraw-Hill, 2008.

[4] L. Luna, “NEXTEL interference debate rages on,” Mobile Radio Technology, Aug. 1, 2003. [5] W. C. Stone, “Electromagnetic signal attenuation in construction materials, NIST construction automation program,” National Institute of Standards and Technology, Gaithersburg, Maryland, Tech. Rep. 3, Oct. 1997. [6] J. Reed, Software Radio: A Modern Approach to Radio Engineering. [7] N. Gershenfeld, The Physics of Information Technology.

Prentice Hall, 2002.

Cambridge University Press, 2000.

Chapter 3 Conventional and Dynamic Spectrum Management Principles 3.1

IMPORTANCE OF SPECTRUM ACCESS TO COGNITIVE RADIO CONCEPTS

It would be a fair question to ask why a technical treatment of an electrical engineering and computer science topic would have a chapter on regulatory and legal processes early in its development of the subject. This is because many of the benefits of cognitive radio are directly related to the real and perceived weakness in the spectrum management process, and even the feasibility of manual spectrum management and the “exclusive use” model that has evolved throughout the world. Early in their deployment, cognitive radio technologies will have to develop methods to operate within this structure. Establishing new regulatory regimes may simultaneously be one of the primary impediments and one of the primary impetuses to the application of cognitive radio technologies. The following discussion is a very general treatment of a subject that has very complex legal and regulatory terminology, practices, rights, and policies. It is not the intent of this section to provide insight into these subtleties, but to provide a general overview of the critical issues that both constrain and motivate cognitive radio. An appreciation of the spectrum management process is also useful background for understanding the evolution of the cognitive radio literature, which is provided in the next chapter. The process for spectrum access is important for cognitive radio through at least two mechanisms: 1. One of the advantages of cognitive radio is that it can provide technology to share spectrum without having to reduce or relocate current spectrum users 41

42

Quantitative Analysis of Cognitive Radio and Network Performance

by providing spectrum access on a not-to-interfere basis (NIB). Whatever degree of access could be achieved through this dynamic sharing would be essentially free.1 This is currently a significant driver for commercial interest in cognitive radio, which is focused on the technology as a platform for DSAbased spectrum sharing.2 2. Another less recognized advantage is that cognitive radio provides a large number of potential environmental adaptations, each of which can provide significant mitigation of otherwise highly challenging conditions (e.g., avoidance of OOB and adjacent channel effects, dynamic topologies, and spectral footprint control). These diverse opportunities are a major focus of the upcoming chapters. The potential benefits of these adaptations are not as bounded as spectrum access. Both of these benefits presuppose that radios have the flexibility to select their own frequencies. If cognitive radios were the first radios ever deployed, then this might be a reasonable assumption. However, long before cognitive radios were contemplated, procedures, policies, law, and even international treaties have evolved to minimize interference among less intelligent devices. At least initially, cognitive radios will have to exist within these legacy environments. The constraints of spectrum management are simultaneously a limitation on DSA operation and an incentive for its use. 3.2

CONVENTIONAL SPECTRUM MANAGEMENT PRINCIPLES AND PRACTICES

3.2.1

Overview

We begin with an introduction to spectrum management as it is practiced worldwide. Spectrum management is a country-specific responsibility, and the implementation of this responsibility varies greatly worldwide. Some national regimes are more advanced, some are more or less stressed by spectrum shortages and they report to different interests within their respective governments, but the basic policy and regulatory principles are remarkably similar worldwide. Lazarus provides an interesting engineering perspective on the process of spectrum regulation [1]. 1 2

Free in the sense that no users would be disrupted, relocated, or removed from the spectrum. In this book, we will refer to this application as “linear DSA” as its benefits are linear with the amount of spectrum that can be provided by DSA without causing impact on other users of the spectrum. The benefits of this are bounded by the inverse of the current spectrum occupancy. This means that such operating regimes could offer up to 10 times increase in spectrum access.

Conventional and Dynamic Spectrum Management Principles

43

Although there are differences, the closest metaphor for spectrum management is real-estate practice. A government entity essentially grants complete control and usage (as in a land title) of a segment of the spectrum (a property) for specific and stated purposes (zoning). Like real-estate zoning, there are limits on what can be done, since the actions on one property can have detrimental effects on the utility, or value, of others. Although these licenses are typically for a fixed time duration, they are generally renewed. In the United States, for example, there is actually a legal “presumption of renewal.” Economists argue the fine points of whether “property rights” is a complete analogy to spectrum (for an example, see Faulhaber and Farber [2]), but from an engineering perspective they closely parallel each other. 3.2.2

Spectrum Allocations

The process begins with an allocation of spectrum to categories of use. These could be satellite uplink, land mobile radio (LMR), fixed services, cellular, and so forth. A good depiction of them is provided by the U.S. allocation, which is depicted in a complex chart provided by one of the two U.S. spectrum regulators, the National Telecommunications and Information Administration (NTIA), shown in Figure 3.1 [3].3 This allocation process has two fundamental concerns. In-Band Compatibility: By grouping like uses together, the allocation process increases the chances that the uses within the band will have the best chance of compatibility. It avoids a 10-mW device having to be located in spectral proximity to a megawatt transmitter. Manufacturers can make assumptions about the range of frequencies that their customers will be assigned, so they can tailor the equipment at the least cost. Out-of-Band Compatibility: By grouping uses, each band has reasonable expectations about the signals in other bands, and signals that may leak into theirs. As shown in Chapter 2, realistic receivers are sensitive to out-of-band signals, and the allocation process provides a mechanism to recognize and mitigate these issues. The charts reflect both the uses and methods of spectrum management. For example, although “fixed” use is not instructive on how spectrum is being utilized, it does recognize that fixed installations can be analyzed in advance for potential spectrum conflicts, whereas mobile uses are ad hoc and cannot be individually analyzed or resolved in advance. 3

A color version of this chart is provided on the DVD and is also available on the Web at the National Telecommunications and Information Administration (NTIA) web site.

UN

DE

IC

PA

ATI O

EN RT M

NS & IN F O

T OF C O

AT

E

RM

MM

30.56

AERONAUTICAL MOBILE (R)

3.230

RADIOLOCATION

Amateur

FIXED

MOBILE SATELLITE

MOBILE

Radiolocation

Maritime Radionavigation (Radio Beacons)

Aeronautical Mobile

300

AMATEUR

FIXED

FIXED SATELLITE (S-E)

37.0 37.5 38.0 38.25 39.0

MARITIME MOBILE

4.0

405 Aeronautical Mobile

4.063

RADIONAVIGATION

415

ACTIVITIES

FREQUENCY 0

WAVELENGTH

BAND DESIGNATIONS

FIXED

MOBILE

AERONAUTICAL RADIONAVIGATION

MARITIME MOBILE

ISM – 40.68 ± .02 MHz

FIXED

MARITIME MOBILE 435

3 x 107m

3 kHz

1 kHz 10 kHz

30,000 m

MOBILE (DISTRESS AND CALLING) 505 510

MARITIME MOBILE MARITIME MOBILE (SHIPS ONLY)

AERONAUTICAL RADIONAVIGATION (RADIO BEACONS) AERONAUTICAL RADIONAVIGATION (RADIO BEACONS)

MOBILE

9

525 535

3,000 m

100 kHz

LF

5.68 5.73 5.90

BROADCASTING

3m

P L

3 cm

10 GHz

X

SHF

Microwaves

S C

30 cm

1 GHz

UHF

FIXED SATELLITE (E-S)

FIXED

MOBILE SATELLITE (E-S)

MOBILE

ISM – 6.78 ± .015 MHz

100 MHz

VHF

MAGNIFIED ABOVE

10 MHz

30 m

FM Broadcast

HF

6.2

764 73.0

14

6.685 6.765

AERONAUTICAL MOBILE (OR)

FIXED

Mobile AMATEUR

AMATEUR SATELLITE

7.0 7.1

AMATEUR

72.0

FIXED FIXED

Mobile Mobile

BROADCASTING BROADCASTING

7.3 7.35

19.95

STANDARD FREQ. AND TIME SIGNAL (20 kHz) 20.05

MARITIME MARITIMEMOBILE MOBILE

8.1 8.195

1013Hz

3 x 105Å

INFRARED

1 THz

0.03 cm

Sub-Millimeter Infrared

88.0

AERONAUTICAL MOBILE (R)

8.815 30

Visible

VISIBLE

1014Hz 1015Hz

3 x 103Å

MOBILE

MOBILE SATELLITE

9.4 BROADCASTING

9.5

1016Hz

3 x 102Å

Ultraviolet

1017Hz

3 x 10Å

RADIO ASTRONOMY

SPACE RESEARCH (Passive)

Mobile **

FIXED

EARTH EXPLORATION SATELLITE (Passive)

FIXED SATELLITE (S-E)

AERONAUTICAL RADIONAVIGATION

FIXED SATELLITE (S-E)

Mobile*

AERONAUTICAL RADIONAVIGATION

9.9 9.995 10.003 10.005 10.1 10.15

30

FIXED

1020Hz

3 x 10 -2Å

FIXED

MOBILE

RADIOLOCATION

INTERSATELLITE

GAMMA-RAY

Gamma-ray

1019Hz

3 x 10 -1Å

X-ray

X-RAY

3Å

1018Hz

ISM – 122.5 ± .500 GHz

11.175 11.275 11.4 11.6 11.65

BROADCASTING BROADCASTING

FIXED

12.05 12.10

FIXED

12.23

AERONAUTICAL MOBILE (OR) AERONAUTICAL MOBILE (R) RADIOASTRONOMY

1021Hz

3 x 10 -3Å

MOBILE

MOBILE SATELLITE

RADIONAVIGATION

1022Hz

3 x 10 -4Å

FIXED Mobile* BROADCASTING FIXED BROADCASTING BROADCASTING Mobile* FIXED AMATEUR AMATEUR SATELLITE AMATEUR FIXED

FIXED

13.2 13.26 13.36 13.41 13.57 13.6 13.8 13.87 14.0 14.25 14.35

STANDARD FREQ. AND TIME SIGNAL (60 kHz)

FIXED

RADIONAVIGATION SATELLITE

Radiolocation

Space Research

ISM – 13.560 ± .007 MHz

132.0125

FIXED

128.8125

136.0 137.0 137.025 137.175 137.825 138.0

Mobile*

1024Hz

3 x 10-6Å

Cosmic-ray

1023Hz

FIXED

FIXED SATELLITE (S-E)

FIXED

BROADCASTING

MOBILE

1605 1615

59

1025Hz

3 x 10 -7Å

90

FIXED

Fixed

FIXED

MET. SAT. (s-E)

MOBILE

MOBILE

RADIOLOCATION

FIXED

110

RADIONAVIGATION

MOBILE SATELLITE

MOBILE

RADIONAVIGATION SATELLITE

130

RADIO ASTRONOMY

SPACE RESEARCH (Passive)

FIXED

MOBILE

190

2501

MOBILE

MOBILE SATELLITE

FIXED FIXED

300 GHz

MOBILE

30 GHz

3 GHz

300 MHz

30 MHz

3 MHz

3000

300 kHz

ISM – 27.12 ± .163 MHz

MOBILE

FIXED

MOBILE

FIXED

MOBILE

FIXED RADIONAVIGATION SATELLITE (E-S)

2502 2505

MARITIME MOBILE

RADIORADIONAVIGATION ASTRONOMY SATELLITE

STANDARD FREQ. AND TIME SIGNAL

LAND MOBILE

MOBILE SATELLITE

ISM – 24.125 ± 0.125 GHz

Space Research

STANDARD FREQ.

2850 AERONAUTICAL MOBILE (R)

AERONAUTICAL RADIONAVIGATION

200

Aeronautical Mobile

ISM – 2450.0 ± 50 MHz

AERONAUTICAL RADIONAVIGATION 2495

STANDARD FREQ. AND TIME SIGNAL (2500kHz)

AERONAUTICAL RADIONAVIGATION

ISM – 245.0 ± 1GHz

FIXED

MOBILE

MARITIME MOBILE

LAND MOBILE

FIXED

EARTH EXPLORATION SATELLITE (Passive)

160

MARITIME MOBILE

PLEASE NOTE: THE SPACING ALLOTTED THE SERVICES IN THE SPECTRUM SEGMENTS SHOWN IS NOT PROPORTIONAL TO THE ACTUAL AMOUNT OF SPECTRUM OCCUPIED.

FIXED

MOBILE

FIXED SATELLITE (E-S)

2107 MARITIME MOBILE

FIXED

MARITIME MOBILE

BROADCASTING (TV CHANNELS 7-13)

FIXED

MARITIME MOBILE

RADIONAVIGATION Radiolocation

1705

MOBILE

FIXED

22.855 23.0 23.2 23.35

24.89 24.99 25.005 25.01 25.07 25.21 25.33 25.55 25.67 26.1 26.175 26.48 26.95 26.96 27.23 27.41 27.54 28.0 29.7 29.8 29.89 29.91 30.0

61

70

FIXED

COSMIC-RAY

3 x 10 -5Å

14.990 15.005 15.010 15.10 15.6 15.8

16.36

BROADCASTING

1900

2065

MARITIME MOBILE (TELEPHONY) LAND MOBILE

2170 2173.5 2190.5 2194

MARITIME MOBILE (TELEPHONY) MOBILE (DISTRESS AND CALLING) MARITIME MOBILE (TELEPHONY)

MARITIME MOBILE

MOBILE FIXED

Radiolocation

BROADCASTING

MARITIME MOBILE 17.41 17.48 17.55

TRAVELERS INFORMATION STATIONS (G) AT 1610 kHz

STANDARD FREQ. AND TIME SIGNAL (15,000 kHz) Space Research STANDARD FREQ. AERONAUTICAL MOBILE (OR)

BROADCASTING FIXED

BROADCASTING BROADCASTING

1800 AMATEUR

RADIOLOCATION

MARITIME MOBILE

FIXED

AMATEUR SATELLITE

21.45 21.85 21.924 22.0

BROADCASTING FIXED AERONAUTICAL MOBILE (R)

216.0 Amateur

MOBILE** LAND MOBILE MOBILE AMATEUR SATELLITE LAND MOBILE FIXED

FIXED

MARITIME MOBILE

144.0 146.0 148.0 149.9 150.05 150.8 152.855 154.0

FIXED FIXED

17.9 17.97 18.03 18.068 18.168 18.78 18.9 19.02

2000 MOBILE

21.0

MOBILE

220.0 222.0 225.0

FIXED MOBILE** FIXED FIXED FIXED AMATEUR

MARITIME MOBILE

MET. SAT. (S-E) MET. SAT. (S-E) MET. SAT. (S-E) MET. SAT. (S-E)

MOBILE

LAND MOBILE

156.2475 157.0375 157.1875 157.45 161.575 161.625 161.775 162.0125

173.2 173.4 174.0

19.68 19.80 19.990 19.995 20.005 20.010

Mobile

FIXED

FIXED

MARITIME MOBILE

ULTRAVIOLET

RADIONAVIGATION

RADIONAVIGATION SATELLITE

Radiolocation

BROADCASTING (FM RADIO)

BROADCASTING (AM RADIO)

3 x 104Å

RADIO ASTRONOMY

SPACE RESEARCH (Passive)

EARTH EXPLORATION SATELLITE (Passive)

ISM – 915.0 ± 13 MHz

MARITIME MOBILE

FIXED

BROADCASTING (TV CHANNELS 5-6)

300 GHz

0.3 cm

100 GHz

Radar Radar Bands

EHF

FIXED

Mobile

74.6 74.8 75.2 75.4 76.0

FIXED FIXED

Amateur

8.965 9.040

FIXED

BROADCASTING FIXED STANDARD FREQ. AND TIME SIGNAL (10,000 kHz) Space Research STANDARD FREQ. AERONAUTICAL MOBILE (R) AMATEUR

AERONAUTICAL MOBILE (OR) AERONAUTICAL MOBILE (R) FIXED BROADCASTING

FIXED

117.975

121.9375 123.0875 123.5875

MARITIME MOBILE

AERONAUTICAL MOBILE (R) AERONAUTICAL MOBILE (R) AERONAUTICAL MOBILE (R) AERONAUTICAL MOBILE (R)

FIXED

1660 1660.5 1668.4

1675

2020

STANDARD FREQ.

AMATEUR

Radiolocation FIXED

Mobile

Radiolocation Radiolocation

FIXED

300

MARITIME MOBILE

MOBILE

MOBILE

FIXED

776

MOBILE FIXED AERONAUTICAL RADIONAVIGATION FIXED MOBILE FIXED MOBILE

794 806

FIXED

AERONAUTICAL MOBILE

8.215

FIXED FIXED

LAND MOBILE RADIOLOCATION

AERONAUTICAL MOBILE (OR)

FIXED

1240

1390 1392 1395 1400 1427 1429.5

SPACE OPN. (S-E) SPACE OPN. (S-E) SPACE OPN. (S-E) SPACE OPN. (S-E)

AMATEUR SATELLITE AMATEUR MOBILE FIXED MOBILE SATELLITE (E-S) MOBILE

RADIONAV-SATELLITE

LAND MOBILE

LAND MOBILE FIXED

MARITIME MOBILE LAND MOBILE FIXED LAND MOBILE MARITIME MOBILE LAND MOBILE MARITIME MOBILE LAND MOBILE

1670

1700

2000

2110

2160 2180

Amateur

3000

MOBILE**

AMATEUR STANDARD FREQ. AND TIME SIGNAL (25,000 kHz)

2385 2390 2400

FIXED AMATEUR AMATEUR

Radiolocation

Radiolocation

Fixed

THE RADIO SPECTRUM

300 m

1 MHz

MF

5.95

TV BROADCASTING

ISM – 61.25 ± .250 GHz 59-64 GHz IS DESIGNATED FOR UNLICENSED DEVICES

FIXED

MOBILE*

608.0 614.0

BROADCASTING (TV CHANNELS 2-4)

ISM – 5.8 ± .075 GHz

AM Broadcast Ultra-sonics

BROADCASTING (TV CHANNELS 21-36)

AMATEUR

MOBILE**

3 x 105m

Audible Range Sonics

100 Hz

3 x 106m

LAND MOBILE

495

MOBILE 4.995 5.003 5.005 5.060

FIXED

VERY LOW FREQUENCY (VLF)

10 Hz

Infra-sonics

MOBILE

FIXED SATELLITE (S-E)

FIXED

Meteorological Satellite (S-E)

FIXED

MOBILE*

4.85

FIXED

FIXED

5.45

RADIO ASTRONOMY

BROADCAST MOBILE

RADIO ASTRONOMY

BROADCAST MOBILE

LAND MOBILE LAND MOBILE FIXED

8.175

AERONAUTICAL MOBILE

8.5

9.2

FIXED FIXED FIXED

10.68

LAND MOBILE (TLM)

MOBILE

SPACE RES. (S-E) SPACE RES. (S-E) SPACE RES. (S-E) SPACE RES. (S-E)

FIXED AMATEUR MOBILE SATELLITE (E-S)

FIXED

MARITIME MOBILE MARITIME MOBILE

MOBILE SAT. (E-S) SPACE RESEARCH (Passive)

RADIO ASTRONOMY RADIO ASTRONOMY

METEOROLOGICAL AIDS (Radiosonde)

Land Mobile MOBILE

1850

2025

2155

S)

MOBILE

MOBILE SATELLITE (S-E)

Fixed

LAND MOBILE AMATEUR

FIXED Mobile* FIXED AERONAUTICAL MOBILE (OR) FIXED AMATEUR SATELLITE

235.0

2360

Space Research STANDARD FREQ. LAND MOBILE MARITIME MOBILE LAND MOBILE FIXED MOBILE** RADIO ASTRONOMY BROADCASTING MARITIME MOBILE LAND MOBILE MOBILE**

Fixed

MOBILE

2483.5 2500 2655 2690 2700

2900

MARITIME RADIONAVIGATION

AERONAUTICAL MOBILE (R) AERONAUTICAL MOBILE (OR) FIXED AMATEUR AMATEUR SATELLITE Mobile FIXED MARITIME MOBILE BROADCASTING FIXED FIXED MARITIME MOBILE FIXED STAND. FREQ. & TIME SIG. Space Research STANDARD FREQUENCY & TIME SIGNAL (20,000 KHZ) Space Research

1755

MOBILE

MOBILE

MOBILE SATELLITE (E-S)

MOB. FX.

MOBILE FIXED

FIXED

23.55

MOBILE**

2290 2300 2305 2310 2320 2345

R- LOC. B-SAT

FX

MOB

RADIOLOCATION

MOBILE

MOBILE SATELLITE (S-E)

RADIODETERMINATION SAT. (S-E)

FIXED

MOB** B- SAT. FX FX-SAT RADIO ASTRON. SPACE RESEARCH EARTH EXPL SAT

AERONAUTICAL METEOROLOGICAL Radiolocation AIDS RADIONAVIGATION

3

325 335

3.5 35.0

LAND MOBILE

FIXED

36.0

401.0

AERONAUTICAL RADIONAVIGATION (RADIO BEACONS)

3.4 AERONAUTICAL MOBILE (R)

MOBILE

399.9 400.05 400.15

MOBILE

MOBILE LAND MOBILE

4.65 4.7

AERONAUTICAL MOBILE (R)

4.75

46.6 47.0

STANDARD FREQ. AND TIME SIGNAL (5000 KHZ) Space Research STANDARD FREQ.

49.6

AERONAUTICAL MOBILE (R)

FIXED

MOBILE* LAND MOBILE

3.0 3.025

3.155

Radiolocation

* EXCEPT AERO MOBILE (R)

30.0

FIXED

** EXCEPT AERO MOBILE

RADIOLOCATION

30 GHz

3 GHz

MOBILE

AERONAUTICAL MOBILE (OR)

FIXED

MOBILE**

Radiolocation

300 MHz

FIXED

MOBILE SATELLITE

MOBILE

30 MHz

FIXED

LAND MOBILE

MOBILE*

33.0

34.0

MET. SAT. (S-E)

FIXED

FIXED

4.438

FIXED

MOBILE*

43.69

AERONAUTICAL MOBILE (OR) MOBILE

(TV CHANNELS 14 - 20)

512.0

4.99

50.0

54.0

AERONAUTICAL MOBILE (OR)

5.65

BROADCASTING

MARITIME MOBILE 6.525

MOBILE

FIXED

7.45

MOBILE

FIXED LAND MOBILE

8.025

821 824 849 851 866 869 894 896 901901 902

LAND MOBILE FIXED LAND MOBILE LAND MOBILE FIXED

8.4 8.45 9.0

FIXED LAND MOBILE MOBILE LAND MOBILE

10.45 10.5 10.55 10.6

FIXED

(E-S)

Fixed (TLM) FIXED (TLM)

MOB. SAT. (S-E) Mob. Sat. (S-E) MOB. SAT. (S-E) Mob. Sat. (S-E)

1530 1544 1549.5

MOBILE SATELLITE (Space to Earth)

1558.5 1559 1610 1610.6 1613.8 1626.5

MOBILE SATELLITE (E-S)

17.1

FIXED

MOBILE** METEOROLOGICAL SATELLITE (s-E)

MOBILE

FIXED

FIXED

SPACE OP. (E-S)(s-s)

FIXED 22.55

INTER-SATELLITE

24.75

BCST-SATELLITE

Fixed

Radiolocation Mobile Fixed

24.65 SATELLITE (E-S)

2417 2450

Radiolocation

MOBILE

FIXED

FX-SAT (S - E)

BCST - SAT. MOBILE** E-Expl Sat Radio Ast Space res.

30.0

FIXED

Figure 3.1 U.S. National Telecommunications and Information Administration (NTIA) frequency allocations.

October 2003

National Telecommunications and Information Administration Office of Spectrum Management

1st Capital with lower case letters

U.S. DEPARTMENT OF COMMERCE

Capital Letters

Mobile

This chart is a graphic single-point-in-time portrayal of the Table of Frequency Allocations used by the FCC and NTIA. As such, it does not completely reflect all aspects, i.e., footnotes and recent changes made to the Table of Frequency Allocations. Therefore, for complete information, users should consult the Table to determine the current status of U.S. allocations.

DESCRIPTION

EXAMPLE

MOBILE SATELLITE

FIXED SATELLITE

FIXED

MOBILE

FIXED

ALLOCATION USAGE DESIGNATION

SPACE RESEARCH

METEOROLOGICAL SATELLITE

EARTH EXPLORATION SATELLITE

Secondary

SERVICE

Primary

SPACE OPERATION

METEOROLOGICAL AIDS

BROADCASTING SATELLITE

NON-GOVERNMENT EXCLUSIVE

RADIONAVIGATION SATELLITE

MARITIME RADIONAVIGATION

BROADCASTING

GOVERNMENT/ NON-GOVERNMENT SHARED

RADIONAVIGATION

MARITIME MOBILE SATELLITE

AMATEUR SATELLITE

GOVERNMENT EXCLUSIVE

RADIOLOCATION SATELLITE

MARITIME MOBILE

AMATEUR

STANDARD FREQUENCY AND TIME SIGNAL SATELLITE

RADIOLOCATION

LAND MOBILE SATELLITE

AERONAUTICAL RADIONAVIGATION

STANDARD FREQUENCY AND TIME SIGNAL

RADIODETERMINATION SATELLITE

LAND MOBILE

300.0

Space Opn. (S-E)

LAND MOBILE

FIXED

FIXED

STD. FREQ. & TIME SIGNAL SAT. (400.1 MHz) (S-E)

FIXED

328.6 335.4

MOBILE SATELLITE (E-S)

RADIONAVIGATION SATELLITE SPACE RES.

MOBILE

FIXED

MOBILE

AERONAUTICAL RADIONAVIGATION

3.5 3.6

MOBILE. SAT. (S-E)

MOBILE

FIXED 402.0

RADIO ASTRONOMY

40.0

42.0

LAND MOBILE

FIXED

LAND MOBILE

BROADCASTING

FIXED

4.94

MOBILE

FIXED

5.0 5.15 5.25 5.35 5.46 5.47 5.6 5.83

6.525 6.70 6.875

MARITIME MOBILE

FIXED

MARITIME MOBILE

FIXED

3 MHz

Radiolocation

Radiolocation

32.0

322.0

FIXED

Radiolocation FIXED SAT. (S-E)

MET. AIDS (Radiosonde)

3.7

Earth Expl. Met-Satellite Earth Expl Sat (E-S)Satellite(E-S) (E-S) Met-Satellite Earth Expl Sat (E-S) (E-S)

450.0 454.0 455.0 456.0 460.0 462.5375 462.7375 467.5375 467.7375 470.0

FIXED

LAND MOBILE

MOBILE

FIXED SAT (S-E) Radiolocation

RADIORadioLOCATION location Radiolocation RADIONAVIGATION MARITIME Radiolocation RADIONAVIGATION

AERONAUTICAL RADIONAV.

Radiolocation Amateur

FIXED

FIXED SATELLITE (E-S)

300 kHz

RADIOLOCATION

RADIOLOCATION

3.65

FIXED

MET-SAT. EARTH EXPL (E-S) SAT. (E-S)

LAND MOBILE FIXED LAND MOBILE LAND MOBILE FIXED LAND MOBILE FIXED LAND MOBILE

4.8

FIXED

48.2

MOBILE**

RADIO ASTRONOMY Space Research (Passive) AERONAUTICAL RADIONAVIGATION

50.4

METEOROLOGICAL AIDS

RADIOLOCATION

5.85 5.925 6.425

MOBILE

FIXED SATELLITE (E-S) MOBILE

Aeronautical Mobile

AERO. RADIONAV.(Ground)

FIXED SAT. (S-E)

MOBILE**

MET-SAT. EARTH EXPL SAT. (E-S) (E-S)

MET. AIDS (Radiosonde)

MET. AIDS SPACE OPN. (Radio(S-E) sonde)

420.0

Amateur

LAND MOBILE FIXED LAND MOBILE LAND MOBILE FIXED LAND MOBILE

4.4

MOBILE 4.5

47.0

LAND MOBILE Radio Astronomy LAND MOBILE

403.0

METEOROLOGICAL AIDS (RADIOSONDE) 406.0 MOBILE SATELLITE (E-S) 406.1 RADIO FIXED MOBILE ASTRONOMY 410.0 SPACE RESEARCH FIXED MOBILE (S-S) 4.2

RADIOLOCATION

AERONAUTICAL RADIONAVIGATION

FIXED

43.5 45.5 46.9 47.2

FIXED

50.2

AERO. RADIONAV. RADIOLOCATION

52.6

51.4

MARITIME RADIONAVIGATION

FIXED SATELLITE (E-S)

FIXED

FIXED SATELLITE (S-E)(E-S)

MARITIME RADIONAVIGATION (RADIO BEACONS)

EARTH-ES

54.25 55.78 56.9 57.0

RADIOAmateur- sat (s-e) Amateur LOCATION Amateur MOBILE FIXED SAT(E-S)

FIXED SATELLITE (E-S)

64.0 65.0 66.0

RADIO ASTRONOMY

Aeronautical Radionavigation

RES.

EARTH-ES

INTERSATELLITE

AERONAUTICAL MOBILE (R) 698

7.125

FIXED Radiolocation Radiolocation

9.3 9.5

FIXED

Amateur Satellite

FIXED

AERONAUTICAL RADIONAVIGATION (RADIO BEACONS)

SPACE

INTERSATELLITE

7.025 7.075

FIXED

7.19 7.235 7.25 7.30

FIXED

7.55 7.75 7.90

Fixed Mobile Satellite (E-S)

SPACE RESEARCH (S-E) (deep space only)

RADIOLOCATION

Radiolocation

Amateur

RADIOLOCATION EARTH EXPL. SAT. (Passive)

Aeronautical Radionavigation (Radio Beacons)

INTER- SAT

INTER- SAT

INTERSATELLITE

FIXED

Fixed

EARTH EXPL. SAT. (S-E)

FIXED Mobile MET. SATELLITE FIXED SATELLITE Satellite (E-S) (E-S) (E-S) (no airborne) FIXED Mobile Satellite SATELLITE FIXED (E-S)(no airborne) (E-S)

FIXED SPACE RESEARCH (S-E)

86.0

Radiolocation

Meteorological Aids

Radiolocation

RADIOLOCATION

SPACE RESEARCH (Passive)

108.0

RADIOLOCATION FIXED-SAT

SPA CE RESEARCH ( Passive)

FIXED (TLM)

1430 1432 1435 1525

1535

1545

Mobile Satellite (S- E)

AERONAUTICAL MOBILE SATELLITE (R) (space to Earth)

15.35

AERONAUTICAL MOBILE (R) AERONAUTICAL MOBILE AERONAUTICAL MOBILE

1350

MOBILE **

EARTH EXPL SAT (Passive)

(S-E)

FIXED**

MOBILE (AERONAUTICAL TELEMETERING)

MOBILE SATELLITE (S-E) AERONAUTICAL MOBILE SATELLITE (R) (space to Earth) AERONAUTICAL MOBILE SATELLITE (R) (space to Earth)

14.4 14.47 14.5 14.7145

AERONAUTICAL RADIONAVIGATION RADIONAV. SATELLITE (Space to Earth) AERO. RADIONAVIGATION RADIO DET. SAT. (E-S) M O B I L E S A T ( E - S ) AERO. RADIONAV. RADIO DET. SAT. (E-S) MOBILE SAT. (E-S) RADIO ASTRONOMY Mobile Sat. (S-E) AERO. RADIONAV. RADIO DET. SAT. (E-S) MOBILE SAT. (E-S)

15.1365

15.4 15.43 15.63 15.7 16.6

METEOROLOGICAL AIDS (RADIOSONDE)

RADIO ASTRONOMY

FIXED FIXED

1710

FIXED

MOBILE SAT. (S-E)

SPACE RES.

MOBILE

FIXED SPACE RES. EARTH EXPL. (E-S)(s-s) SAT. (E-S)(s-s)

FIXED MOBILE

2200

SPACE EARTH OPERATION EXPLORATION (s-E)(s-s) SAT. (s-E)(s-s)

SPACE RESEARCH (s-E)(s-s)

MOBILE (LOS)

FIXED Amateur

SPACE RES..(S-E)

Radiolocation

Mobile

24.25 24.45 INTER-SATELLITE

RADIOLOCATION

RADIONAVIGATION

INTER- SAT EARTH EXPL-SAT (Passive)

RES.

RES.

59.3

RADIOLOCATION

FIXED

FIXED

FIXED SAT (E-S)

MOBILE 71.0

746

Mobile Satellite (S-E)

FIXED

MOBILE SATELLITE (S-E)

FIXED SATELLITE (S-E)

Mobile Satellite (S-E) Mobile Satellite (S-E)

FIXED FIXED MOBILE SATELLITE (E-S) SATELLITE (E-S) FIXED EARTH EXPL. FIXED SATELLITE (E-S) SATELLITE(S-E)

EARTH EXPL. SATELLITE (S-E)

84.0

BROADCASTING SATELLITE

BROADCASTING

AERONAUTICAL RADIONAVIGATION MARITIME RADIONAVIGATION

RADIONAVIGATION

92.0

928 929 930 931 932 935 940 941 944 960

FIXED FIXED FIXED FIXED

10.0 Amateur

Radiolocation

102.0

10.7

FIXED-SAT

13.25

Radiolocation

Radiolocation Radioloc.

Radiolocation

MOBILE

FIXED

ASTRONOMY

LAND MOBILE LAND MOBILE (TLM)

12.75 FIXED

Land Mobile Satellite (E-S)

FIXED SAT (E-S)

Radiolocation

RADIOLOC.

Amateur 1300

FIXED

FIXED

Mobile Mobile

Space Research EARTH EXPL. SAT. (Passive)

AERONAUTICAL RADIONAVIGATION AERONAUTICAL RADIONAVIGATION

Space Res.

FIXED

FIXED

LAND MOBILE RADIO

FIXED

MOBILE FIXED SATELLITE MOBILE (E-S)

MOBILE SAT. Mobile ** (Space to Earth) MOBILE SAT. Mobile MARITIME MOBILE SAT. (Aero. TLM) (Space to Earth) (Space to Earth) MARITIME MOBILE SATELLITE MOBILE SATELLITE (S-E) (space to Earth)

14.2

FIXED SATELLITE (E-S)

Mobile**

149.0

Mobile

FIXED

RADIOLOCATION RADIOLOCATION Space Res.(act.) RADIOLOCATION Earth Expl Sat

174.5

19.3 19.7

FIXED SATELLITE (S-E) FIXED SATELLITE (S-E) FX SAT (S-E)

STD FREQ. & TIME

MOBILE FIXED

FIXED (LOS)

24.0

Radiolocation

Amateur

FIXED

NOT ALLOCATED

AMATEUR SATELLITE

MOBILE

FIXED

MOBILE

MOBILE

FIXED

MOBILE

MOBILE

MOBILE**

MOBILE

INTERSATELLITE

FIXED MOBILE SATELLITE SATELLITE (S-E) (S-E)

RADIOLOCATION

MOBILE

LAND MOBILE LAND MOBILE

Radiolocation

RADIOLOCATION

95.0

100.0

105.0

EARTH EXPL. SATELLITE (Passive)

SPACE RESEARCH (Passive)

RADIOLOCATION

RADIOLOCATION AERONAUTICAL RADIONAVIGATION

12.2

119.98

BROADCASTING SATELLITE FIXED SATELLITE (E-S) SPACE RESEARCH (S-E) (Deep Space)

Space Research

142.0 144.0

150.0

FIXED Land Mobile SAT. (E-S) Satellite (E-S) FX SAT.(E-S) L M Sat(E-S) Space Research Space Research

Mobile Fixed

SPACE RESEARCH (Passive)

RADIO ASTRONOMY

164.0 168.0 170.0

176.5

17.2 17.3 17.7 17.8 18.3 18.6 18.8

FIXED SATELLITE (S-E)

20.1 MOBILE SATELLITE (S-E) 20.2 FXFIXED SAT (S-E) MOBILE SAT (S-E) 21.2 F I X E D MOBILE EARTH EXPL. SAT. FIXED

185.0

21.4 22.0 22.21

S P A C E R A D . A S T MOBILE** F I X E D EARTH EXPL. SAT. 22.5 RES.

RADIO ASTRONOMY

EARTH EXPL. SAT. (Passive)

Amateur RADIOLOCATION MOBILE** FIXED Radiolocation Mobile Fixed MOB FX R- LOC. B-SAT

24.05

235.0

RADIOLOCATION

RADIONAVIGATION Earth Expl. INTER-SATELLITE Satellite (Active)

25.05 25.25 25.5 27.0 27.5 29.5 29.9

MOBILE SATELLITE (E-S)

3 kHz

3.0

MARITIME RADIONAVIGATION

AERONAUTICAL RADIONAVIGATION (Ground)

36.0 37.0

SPACE RESEARCH (space-to-Earth)

MOBILE SATELLITE (E-S)

FIXED SATELLITE (E-S)

RADIONAV. SATELLITE

AMATEUR

FX SAT(E-S)

MOBILE

FIXED

SPACE RESEARCH

SPACE RESEARCH (Passive)

EARTH EXPLORATION SATELLITE (Passive)

SPACE

MOBILE

SPACE RES. MOBILE

FIXED

MOBILE

75.5

AMATEUR SATELLITE

76.0 Amateur 77.0 Amateur Amateur Sat. 77.5 AMATEUR AMATEUR SAT 78.0 Amateur Amateur Satellite 81.0

RADIOLOC. RADIOLOC. RADIOLOC. RADIOLOCATION

MOBILE

FIXED SATELLITE (E-S)

SPACE RESEARCH (Passive) FIXED SATELLITE (S-E)

FIXED

(Passive)

Amateur Satellite

FIXED SATELLITE (S-E)

MOBILE

Fixed Fixed FIXED MOBILE

151.0

AERO RADIONAV SPACE RES. (Passive) MOBILE

EARTH INTERSATELLITE EXPLORATION SAT. (Passive)

MOBILE **

12.7

126.0

Space Research (E-S) AERONAUTICAL RADIONAV. 13.4 RADIOStandard RadioLOCATION Freq. and location Time Signal 13.75 FIXED RADIORadioSatellite (E-S) SAT.(E-S) location LOCATION 14.0 RADIO Land Mobile FIXED NAVIGATION SAT. (E-S) Satellite (E-S)

134.0

AMATEUR SATELLITE Amateur

MOBILE

FIXED FIXED

EARTH EXPL. SAT. SPACE RES. (Passive) (Passive)

RADIO ASTRONOMY

FIXED

SPACE RESEARCH (Passive)

MOBILE

Radiolocation BCST SAT. FX SAT (E-S) FIXED SATELLITE (E-S) FIXED FIXED SATELLITE (S-E) FIXED FIXED SATELLITE (S-E) FX SAT (S-E) EARTH EXPL. SAT. SPACE RES.

182.0

190.0

MOBILE MOBILE**

FIXED 200.0 202.0

23.6

SPACE RES. (Passive)

AMATEUR

AMATEUR SATELLITE

231.0

250.0 252.0

RADIO ASTRONOMY

INTER-SATELLITE

ACTIVITY CODE

U.S .

MOBILE SATELLITE (E-S)

3.1

31.3

33.0 33.4

Radiolocation SPACE RE. EARTH EXPL. .(Passive) SAT. (Passive)

MOBILE

AERONAUTICAL MOBILE SATELLITE

M I N ISTR ATI O N

FIXED SATELLITE (E-S)

Standard Frequency and Time Signal Satellite (S-E)

31.0 MOBILE

31.8

3.3

INTER-SATELLITE

RADIONAVIGATION RADIOLOCATION FIXED

32.0

RADIONAVIGATION 32.3

INTER- SAT

RADIONAVIGATION

MOBILE

AERONAUTICAL MOBILE

AD

30.0

FIXED

Stand. Frequency and Time Signal Satellite (S-E)

SPACE EARTH RADIO RESEARCH EXPLORATION ASTRONOMY (Passive) SAT. (Passive) SPACE RADIONAVIGATION RESEARCH (deep space) SPACE RES.

FIXED

37.6 FIXED SPACE F I X E D MOBILE RES. SATELLITE (S-E) 38.0 FIXED FIXED MOBILE SAT. (S-E) 38.6 FIXED-SATELLITE FIXED MOBILE 39.5 F I X E D MOBILE FIXED MOBILE SATELLITE SAT. 40.0 Earth EARTH SPACE F I X E D MOBILE Expl. EXPL SAT SAT. RES. (E-S) Sat (s - e) SAT (E-S) 40.5 BROAD- FX-SAT Fixed Mobile CASTING (S-E) 41.0 BROADBCST MOBILE CASTING SAT. 42.5 FIXED F I X E D M O B I L E * * SATELLITE (E-S) BCST SAT.

FIXED

RADIO ASTRONOMY

MOBILE MOBILE SAT (E-S). MOBILE FIXED

RADIONAV.SAT. MOB. SAT(E-S)

FX SAT(E-S)

MOBILE

FIXED

EARTH EXPLORATION SATELLITE FI XED MOBILE SATELLITE (E-S) SATELLITE (E-S)

MOBILE

FIXED

SPACE

FIXED FIXED

EARTH F I X E D INTER EXPLORATION - SAT SAT. (Passive) 58.2 SPACE EARTH RESEARCH EXPLORATION (Passive) SAT. (Passive) 59.0 SPACE RADIO- INTERRES.. LOC. SAT

EARTH EXPLORATION F I X E D SAT. (Passive)

EARTH SPACE EXPLORATION RESEARCH F I X E D M O B I L E * * SATELLITE

RADIORADIO MOBILE NAVIGATION SATELLITE NAVIGATION SATELLITE

BROADCAST

MOBILE

FIXED

FIXED SPACE RESEARCH (E-S) FIXED

FIXED

MOBILE

FIXED

AMATEUR

FIXED SATELLITE (S-E)

FIXED MET. FIXED SATELLITE (S-E) SATELLITE (S-E) FIXED FIXED SATELLITE (S-E)

74.0

FIXED SATELLITE (E-S)

MOBILE

FIXED

FIXED

FIXED

EARTH EXPL. SATELLITE (Passive)

RADIO ASTRONOMY

1215

RADIONAVIGATION SATELLITE (S-E) 11.7

116.0 EARTH

SPACE

INTERF I X E D MOBILE SATELLITE RESEARCH EXPL SAT. (Passive) SPACE EARTH Amatuer F I X E D M O - INTERBILE SAT. RES. EXPL .SAT 120.02 EARTH SPACE INTERSAT. F I X E D MOBILE SATELLITE RESEARCH EXPL (Passive) (Passive)

AMATEUR RADIOLOCATION

FIXED SATELLITE (S-E)

EARTH EXPLORATION SATELLITE (Passive)

INTERSATELLITE

MOBILE

FIXED

INTERSATELLITE

MOBILE

AMATEUR

Earth Expl. Satellite (Active)

238.0 241.0 248.0

EARTH EXPLORATION SATELLITE (Passive)

RADIO SERVICES COLOR LEGEND

M

FIXED

INTERSATELLITE

MOBILE

EARTH SPACE RESEARCH EXPLORATION (Passive) SATELLITE (Passive)

Radiolocation

Amateur

Amateur Satellite

MOBILE MOBILE MOBILE MOBILE

FIXED SAT (E-S)

MOBILE

THE RADIO SPECTRUM

OM

FIXED

Radiolocation EARTH EXPL. SAT. (Passive)

FIXED SATELLITE (S-E)

MOBILE

INTER-SAT. INTER-SAT.

FIXED FIXED

FIXED

FIXED SATELLITE (E-S) MOBILE SATELLITE (E-S) FIXED SATELLITE (E-S)

FREQUENCY ALLOCATIONS

TI O N A L TELE C

FIXED

EARTH EXPLORATION SAT. (Passive)

SPACE RES. (Passive)

MOBILE

FIXED SATELLITE (S-E)

MOBILE

FIXED SPACE RES. SATELLITE(S-E) (Passive)

MOBILE

FIXED RADIOLOCATION

AMATEUR SATELLITE

RADIOLOCATION INTER-SATELLITE F I X E D (E-S) SATELLITE SATELLITE (E-S) MOBILE FIXED FIXED SATELLITE (E-S)

RADIONAVIGATION Earth Standard Exploration Frequency and Satellite Time Signal (S-S) F I X ESatellite (E-S) D

INTERSATELLITE

FIXED

Earth Exploration Satellite (S-S)

Earth std Exploration freq e-e-sat (S-S) (s-s) &Satellite time e-e-sat

UNITED STATES

NA

N

E

IO

RC

RADIO ASTRONOMY

FIXED

MOBILE

FIXED 217.0

FIXED FIXED

SPACE RES. (Passive)

265.0

275.0

300.0

Aeronautical Mobile

MARITIME RADIONAVIGATION (RADIO BEACONS)

Maritime Radionavigation (Radio Beacons)

Aeronautical Radionavigation (Radio Beacons)

275 285 300

44

Quantitative Analysis of Cognitive Radio and Network Performance

Conventional and Dynamic Spectrum Management Principles

45

The band allocation process is an international one. RF signals cross borders, satellites orbit the entire planet, aircraft and vehicles transit among nations, commercial manufacturers need world markets, and their products circulate worldwide. The International Telecommunication Union (ITU) Radiocommunication Sector (ITU-R) through periodic World Radio Congress meetings and other proceedings establish allocations that are binding on nation-states as treaty obligations. These allocations may vary by ITU Region (Europe and Northern Asia, South Asia and Australia, Americas) as shown in Figure 3.2.4 Nation-states can generally make other uses of spectrum so long as the uses do not conflict with these obligations. This is important to the adoption of cognitive radio because noninterfering operations therefore can be permitted on a nation-by-nation basis, without direct international action or sanction, so long as the ITU-mandated primary uses are protected.

Figure 3.2 ITU spectrum allocation regions.

4

A color version of this chart is provided on the accompanying DVD and is also available on the Web.

46

3.2.3

Quantitative Analysis of Cognitive Radio and Network Performance

Frequency Assignment

The second tier process is the assignment of frequencies to specific uses, users, or equipment. There may be only one or two cellular providers in a band or there may be literally tens of thousands of land mobile radios (LMRs). Frequency managers attempt to avoid conflicting assignments and still achieve dense spectrum usage. Protecting fixed services such as towers is a straightforward process, but as an increasing number of devices become mobile, it is hard to protect operations in all of the locations that a mobile node could be located. Authorization to use a segment of spectrum can come with different rights and restrictions. A primary user is assumed to be assured no interference. A secondary user is allowed access only as long as there is no harmful interference to the primary user or service, and is generally not assured any level of access or interference-free operation. Much of the cognitive radio and DSA research is directed to creating opportunities for secondary sharing of spectrum that is currently allocated and assigned to primary users. Chapters 13 and 14 quantify some aspects of this deconfliction process. Many of the spectrum and frequency management mechanisms were developed during a period where spectrum was largely used for broadcast, radar, or voice communications. As shown in Figure 3.3, new communications applications are evolving rapidly on the wired Internet and are transitioning to wireless delivery faster and faster. Due to this, the spectrum problem has grown exponentially as these two effects have converged simultaneously. First, many of the applications that were once tethered to hardwire Internet connections are transitioning to wireless delivery. Second, the applications that are desired consume much more bandwidth than conventional voice. Instead of a single broadcast serving millions, each user is getting his or her own multimedia IP stream and having it delivered to a wireless device. Videos are uploaded to social networking sites rather than home movies taken to be developed. And increasingly, that video is not uploaded from a fixed infrastructure connected computer, but directly from the wireless device. Video is supplanting audio as the baseline media. Wireless devices are replacing fixed equipment such as TVs, audio systems, and personal computers. Bandwidth through fiber or wired infrastructures is scalable; if there is demand, then the capacity can be built out to any aggregate throughput. With spectrum, the aggregate throughput cannot exceed the amount of spectrum available. Additional spectrum for one service can only come from displacing another spectrum user.

Conventional and Dynamic Spectrum Management Principles

47

!"#$%&&'()*+,-.$/,0$1-2"0-"2$3,445-()*+,-.$ '??**1#:%)*'$7)"=)")$1)*7/*74)* 5+)1)'2'$,*./0)86*%'$1)*'7*'%*'$* "#$,)*/=*9/74*74)*7"#$%&'3$,* #$0*%)$%'$,*$/0);*

Figure 3.4 Receiving node hidden from DSA sensing.

For DSA to be effective as a noninterfering solution, it must guarantee that: There is no receiver within its range of effect that could be receiving a usable signal on the same frequency. In many treatments of DSA, it is assumed that DSA provides interference free sharing of spectrum by dynamically avoiding occupied frequencies. In later chapters, we will consider that DSA could be spectrum sharing with controlled interference

50

Quantitative Analysis of Cognitive Radio and Network Performance

levels. This control could be exercised to ensure no interference, or it could also permit controlled levels of interference according to the thresholds that were permitted by policy. This definition includes the classic definition, but provides the flexibility to balance impact, risk, and benefit of other than zero interference regimes. The IEEE Standards Coordinating Committee 41 and P1900 Working Group have developed standard definitions and concepts for DSA systems that are a useful point of departure [6, 7].

3.4

OTHER SPECTRUM MANAGEMENT CONSIDERATIONS

The previous sections have addressed spectrum management from the perspective of the regulators and manager of the spectrum. In practice, a number of less formal but quite significant considerations also can interfere with or complicate the regulatory process. 3.4.1

Assumed “Squatter’s Rights”

The principles discussed to this point have been technical and regulatory in their focus. Another element that must be recognized is that there are significant political and societal implications in spectrum decision making. This is most apparent when an installed base of spectrum users is impacted by permitted, but interfering, uses of the spectrum. In the United States, garage door openers are a secondary user of a band that has primary assignments for government fixed/mobile uses. When the military deployed trunking systems in the band, suddenly garage door openers failed to operate due to either direct interference or front-end desensitization. Newspapers ran articles about how the military systems were “jamming” and interfering with the aggrieved homeowners’ equipment, and the FCC had to clarify the situation [8]. There was considerable pressure on the government (the proper spectrum “owners”) to halt deployment, even though the remote door openers had no status in the band, and legally had to accept any interference as a condition of their operation. Interestingly, the use of poor receivers can create a political “right” for services, regardless of their legal status. This is of concern to cognitive radio, since one component of the resistance to spectrum sharing may be attributable to the concern by spectrum “owners” that shared secondary access to their spectrum could result in a perceived “right” to use the spectrum. For example, if a shared secondary public access Internet service was

Conventional and Dynamic Spectrum Management Principles

51

operating on an unused cellular frequency, and then the cellular provider decided to deploy on the frequency, might there be so much public outcry about the loss of the Internet service that the cellular provider would essentially lose access to the spectrum? This extralegal structure is less of a challenge if the cognitive radio has a variety of choices, so that no secondary service would be completely dependent on shared access to any one frequency or band. There are ways to address this issue, such as requiring any public service based on shared secondary access to the spectrum to show that it could maintain service quality in the event of the loss of access to any of the frequencies it is sharing. Imposition of constraints such as this proposal may result in less resistance to spectrum sharing concepts. 3.4.2

Out-of-Band Effects

The primary focus of spectrum management is deconflicting frequencies. In most cases, this approach is sufficient to assure coexistence of spectrum users. However, some interference conditions arise from energy in adjacent signal bands. Some of the engineering issues that can lead to these conditions were discussed in Chapter 2. These effects are often unpredictable (or at least only obvious to the engineers after they occur) and create a conundrum: presumably both users of the spectrum are within their licensed rights, yet the effect is disruptive to at least one of the parties. An example of this situation is the interference between public safety radios and certain cellular base stations used by NEXTEL for cellular service in the United States [9]. The resolution of these issues is both technically and legally complex, and will not be explored further in this book. However, the cognitive radio practitioner can reasonably expect that these conflicts will increase as spectrum usage becomes more dense. Current cellular technology was generally architected with a focus on symmetric services such as voice and text messaging. These can use FDD to ensure that no strong signals are present too close to the receiver frequency. However, with a transition to inherently asymmetric services such as Internet access, there will be more desire to exploit the flexibility of TDD protocols. This will greatly increase the opportunity for adjacent channel effects, since the receiver must operate on the same frequency as its neighbors transmit on. 3.5

EMERGING DSA OPPORTUNITY—TV “WHITE SPACE”

DSA has been pursued along two paths; one seeking general approaches to sharing spectrum, and the other investigating specific opportunities for spectrum sharing

52

Quantitative Analysis of Cognitive Radio and Network Performance

based on the specific characteristics of incumbent systems. The former approach is the primary focus of this book, but it is important to recognize the second approach, which has made progress in achieving acceptance of DSA sharing of unused TV channels. This general DSA topic is referred to as TV “white space.” This spectrum sharing opportunity had evolved from initial concept to initial acceptance by the FCC. The FCC issued a Notice of Proposed Rule Making (NPRM) in 2004 [10] to potentially allow the use of the unused TV bands. After testing several prototypes and initially rejecting the concept due to a damaged prototype, the FCC determined that the prototypes did successfully detect TV transmissions at the required sensitivity [11]. On November 4, 2008, the FCC voted unanimously to approve the use of unused TV spectrum on an unlicensed basis in 2007. This decision is the basis for the IEEE 802.22 standard [12]. While the acceptance of DSA by a national regulator was a significant advance in progress to DSA, there are a significant number of constraints in the final FCC order that will greatly impede the exploitation of this opportunity. In addition to requiring spectrum sensing, the device must access a database to verify that the location has no TV reception (in the band) possible. This is despite the test articles requiring detection of TV broadcasts at signal levels of −114 dBm. Prototype articles showed typical performance several dB better than this strict standard. A typical digital TV receiver required a signal level of approximately −85 dBm, 30 dB (1,000 times) stronger than the detection threshold. Limited permitted transmit power is another constraint. This initial conservative approach to TV band sharing is also indicative of the severe challenges to creating absolute proof of noninterfering operation, if all of the responsibility is allocated to the sharing device. Opponents of spectrum sharing have an almost unlimited set of potential conditions from which to argue that interference might be caused. Later chapters will explore sharing regimes in which the responsibility for interference avoidance and mitigation is shared between all of the devices in the spectrum, regardless of legal status.

3.6

DSA’S ROLE IN COGNITIVE RADIO

The discussion in this book assumes that the device or network is authorized by some process to enter the spectrum and use it, so long as its operation is consistent with a set of policies. Considerable research has gone into the subject of how market and allocation mechanisms can arbitrate allocation of spectrum through centralized and distributed algorithms. Yuan et al. [13] describe a typical structure capable of

Conventional and Dynamic Spectrum Management Principles

53

both central and distributed spectrum allocation based on pairwise interference. Zhao et al. [14] describe a purely distributed process for arriving at coordinated spectrum allocations. Both results demonstrate that the introduction of centralized or distributed coordination does not fundamentally change the process or result in a likelihood of far from optimal solutions. Most DSA research has focused on ensuring noninterference to other spectrum users. In this book, the technology to ensure noninterfering operation is assumed to be sufficient. We will investigate the next set of questions, which focus on how to make the best use of the options that are provided, and what new constructs are enabled by this flexibility. It is important to understand how a radio can determine the relative advantage of each spectrum segment for which it is provided a policy-compliant opportunity. Not all spectrum choices are equal, and the desirability of a given piece of spectrum is a function of more than just the channel’s characteristics. From the node or link perspective, it includes the effects of adjacent band usage, as perceived by the network members, the likelihood of forced relocation, the predicted noise floor, and the denial of spectrum access to other network nodes. From the network perspective, it includes the effect of decisions made by one node on the operation of all other nodes sharing that spectrum. In this discussion, spectrum usage is considered and optimized from a “bandwidth over geographic area” perspective rather than optimized to reduce the occupied bandwidth of the signal. In this book, DSA is an enabler to optimize link and network operation, not an objective in and of itself. This process is independent of the mechanism of spectrum sharing.5 With knowledge of the impacts of spectrum decisions, the device or network can adjust its own independently determined choices, make requests to spectrum brokers, or even make bids into an auction process to best reflect the utility of each spectrum alternative. EXERCISES 3.1

5

Research spectrum allocations. Determine the total allocation of spectrum below 3 GHz, and determine the total spectrum available for different uses.

Such as decentralized, microcharged, brokered, auctioned, secondary market provided.

54

References

3.2

Propose a framework for evaluating the value of different spectrum bands, reflecting their utility for various uses. This metric should reflect the difference in utility between 1 MHz of spectrum at 500 MHz and the same bandwidth at 3 GHz. Justify the metric you have selected.

3.3

Government spectrum regulators generally are required to keep public records of the interference issues that have arisen, and had required their actions or decisions for resolution. Research those applicable within your national spectrum management community, and develop a classification of the core reason for the interference issue, and the actions taken for resolution. Analyze whether cognitive radio could have adapted around this issue.

3.4

Using the MATLAB spectrum occupancy file for Chicago, IL (ChicagoSampleSet.mat), which is described in Appendix B, and the U.S. Table of Allocations [15], examine apparent utilization of each of the allocations in the Table of Allocations. Note: The entire table of allocations is not included in the sensed spectrum, and the Chicago sample has the most complete spectrum coverage. References

[1] M. Lazarus, “Radio’s regulatory roadblocks,” IEEE Spectrum, vol. 46, no. 9, pp. 42–45, 52–56, Sept. 2009. [2] G. R. Faulhaber and D. J. Farber, Spectrum Management: Property Rights, Markets, and the Commons. Working Paper 02-12: AEI-Brookings Joint Center for Regulatory Studies, Dec. 2002. [3] National Telecommunications and Information Administration, United States Frequency Allocations: The Radio Spectrum. www.ntia.doc.gov/osmhome/allochrt.PDF: U.S. Government, Oct. 2003. [4] P. Kolodzy, “Communications policy and spectrum management,” in Cogntive Radio Technology, 2nd ed., B. Fette, Ed. Academic Press, 2009, ch. 2. [5] United States Federal Communications Commission, Spectrum Policy Task Force Report. ET Docket No. 02-135, Nov. 2002. [6] IEEE Standards Coordinating Committee 41, Dynamic Spectrum Access Networks, http://www.scc41.org/, 2007. [7] IEEE, IEEE Standard Definitions and Concepts for Dynamic Spectrum Access: Terminology Relating to Emerging Wireless Networks, System Functionality, and Spectrum Management. IEEE Std 1900.1TM -2008, Sept. 26, 2008. [8] United States Federal Communications Commission, Consumers May Experience Interference to Their Garage Door Opener Controls Near Military Bases. Public Notice, 20 F.C.C.R. 3614, Feb. 15, 2005. [9] L. Luna, “NEXTEL interference debate rages on,” Mobile Radio Technology, Aug. 1, 2003.

References

55

[10] United States Federal Communications Commission, Noticed of Proposed Rulemaking in the Matter of Unlicensed Operation in the TV Broadcast Bands. Docket 04-186, May 13, 2004. [11] FCC Office of Engineering and Technology, Initial Evaluation of the Performance of Prototype TV-Band White Space Devices, July 2007. [12] C. Stevenson, G. Chouinard, Z. Lei, W. Hu, S. Shellhammer, and W. Caldwell, “IEEE 802.22: The first cognitive radio wireless regional area network standard,” IEEE Communications Magazine, vol. 47, no. 1, pp. 130–138, Jan. 2009. [13] Y. Yuan, P. Bahl, R. Chandra, T. Moscibroda, and Y. Wu, “Allocating dynamic time-spectrum blocks in cognitive radio networks,” ACM International Symposium on Mobile Ad Hoc Networking and Computing, Montreal, Canada, 2007. [14] J. Zhao, H. Zheng, and G. Yang, “Distributed coordination in dynamic spectrum allocation networks,” First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, Baltimore, MD, Nov. 2005. [15] U. S. Code, Title 47—Telecommunication Chapter I, Part 2—Frequency Allocations and Radio Treaty Matters; General Rules and Regulations, Para 2.106. http://edocket.access.gpo.gov/cfr 2008/octqtr/pdf/47cfr2.106.pdf: U. S. Government.

Chapter 4 A Short Introduction to Cognitive Radio Development 4.1

OVERVIEW

The cognitive radio field has been in existence for a single decade, since the concept was first proposed by Mitola [1]. In that time, it has created several major international conferences, has won an initial spectrum regulation victory,1 and has received significant funding from a variety of government research organizations for its further research and development. This chapter provides a short summary of some of the progress in developing the science and practice of cognitive radio, the literature that reports this progress, and some of the major results that have been reported. It is not a substitute for a more complete survey of the field. However, it should aid in providing a broad understanding of the scope of cognitive radio research. All of the reported work was performed within the last decade. Therefore, with a 12–24-month publication cycle, there have been only a few generations of derivative work to review. Also, as the field is still in formative stages, no established taxonomy of research efforts has been established or accepted. The organization of this section is the author’s own perception of the structure of the reported research efforts. Figure 4.1 illustrates the structure of this chapter’s discussion. Much of the cognitive radio research falls into four major categories. Note that these categories are not partitions; some work addresses multiple of these categories, as is reasonable in a system-focused field. 1

Regulatory acceptance of TV white space.

57

58

Quantitative Analysis of Cognitive Radio and Network Performance

123(/&'(-)

96(/8.:5)90+.%$#)

A($(.+7)

;%$"'0+C( D6@":1$%(2,31'.(

?"6$'() 0.5 0.4 ;?) 0.3

8A)B)?" 234*+5%"5>"+,("

-%)(4/*'";1$%&"

-%#)*%)"./(%)"

!%"(/(%)")1*)" 56634#9":3)"0$)1" %5"$%)(4/*'"7(4$52"

!"#7(6$86" (/(%)"$%";$,("

./(%)"

?7(6$86"+,(" =5%6(7)"

;(,754*'" .%+) Lmargin ), when IMD3 , plus all other link effects exceed the link margin.

362

Quantitative Analysis of Cognitive Radio and Network Performance

The link can also fail due to lack of spectrum for a cognitive radio, or improper management of spectrum resulting in an unexpected interference event to a noncognitive radio. The availability of the noncognitive radio is significantly impacted by the front-end performance and the degree of acceptable risk in the spectrum deconfliction, while for the cognitive radio, it is the much lower probability of front-end overload noise exceeding the margin and the probability of spectrum pool exhaustion (Aspectrum ). For Pinterfere and P(IMD3 )  1, the interference probability of a link is therefore given by:

Pinterruption = 1 − (1 − (P(IMD3 ) < Lmargin ))(1 − Pinterfere ) where:

Pinterruption P(IMD3 ) Lmargin Pinterfere

(20.1)

Probability of channel or intermodulation noise exceeding Lmargin Probability of IMD3 noise level exceeding the IMD3 level Ratio of excess energy above the minimum to close the link at required BER Probability of spectrum separation mechanism failure

Pinterfere is the probability of failure of the spectrum management process. This is the probability that a noncognitive radio finds an interferer, or a cognitive one cannot locate an opportunity. For a noncognitive radio, Pinterruption is the combined effect of the intermodulation probability and the probability of successful spectrum management, as shown from (10.13). PinterruptionNCR = (1 − Pinterference )IIMD3 (FEα , FEβ ) for 0 ≤ x ≤ 1

when: xIMD3 = where: FEmin , FEmax FEα , FEβ BW



BW fc k1 b0



(20.2)

(PIMD3 − 2 IIP3 + k2 ) − FEmin FEmax − FEmin

Minimum and maximum FE power for the bandwidth Distribution α and β characteristics Bandwidth factor (front-end filter width in relationship to fc )

Performance, Reliability, and Component Trades

fc k1 , k2 b0

363

Center frequency Polynomial coefficients to map input energy to intermodulation energy (from (10.9)) Signaling bandwidth

For a cognitive radio, the Pinterfere term is a function of the size of the pool and the mean value of the demand, as shown in (14.8) and (14.9). Combining these and (11.3) yields: PinterruptionCR = !   IIP3 X IIP3 k Npool −1 (1 − duty) duty (1 − IxIIP3 (FEα , FEβ ))PSS k

(20.3)

k=needed

when : xIIP3 = where:

FEmin , FEmax FEα , FEβ IIP3 needed duty Npool PSS

IIP3 − FEmin , and FEmax < IIP3 < FEmin FEmax − FEmin Minimum and maximum FE power for the bandwidth Distribution α and β characteristics Limit of acceptable front-end input power Quantity of available spectrum channels in the pool Probability of any one node requiring a spectrum assignment Quantity of radios contending for the available spectrum pool Number of independent preselector settings

Both PSS and needed reflect the size of the spectrum pool available to the device. Determination of the binomial distribution parameters was described in Chapter 14. duty Npool loading = (20.4) needed where: loading needed

Mean value of instantaneous spectrum utilization Quantity of available spectrum channels in the pool

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Quantitative Analysis of Cognitive Radio and Network Performance

duty Npool

Probability of any one link requiring a spectrum assignment Quantity of radios contending for the available spectrum pool

The controlling variables can now be written in terms of the two bandwidth category terms (BW and b0 ) from Chapter 10 and the extent of spectrum coverage (flow and fhigh ), developing another expression of the size of the spectrum pool (needed), if the radio had full access to the coverage band.2 needed = where:

needed b0 flow fhigh

fhigh − flow b0

(20.5)

Quantity of available spectrum channels in the pool Signaling bandwidth Lowest frequency in the tuning range Highest frequency in the tuning range

Substituting the previously cited references yields the probability of successful operation of both radio designs as a function of the spectrum made available to them. This formulation also points to the significance of the required signal bandwidth (b0 ). Smaller bandwidths not only improve the chance of locating low energy signaling and front-end channels, they also increase the statistical pool size, and therefore improve the probability of spectrum availability as well. From Chapters 11 and 14, the availability probability of a cognitive radio is driven by the flexibility to tune the front-end through a range of frequency selections and the size of the spectrum pool. The relative availability of the two designs is therefore sensitive to the extent of available spectrum, the relative IIP3 values, and the bandwidth of the device. Figure 20.33 illustrates the availability of cognitive and noncognitive radios as a function of the availability of spectrum. Note that the ratio of mean demand to spectrum availability is constant in this example. If only a few radios are assigned to a spectrum pool, and the number of channels is correspondingly low, the performance of the cognitive radio is well below that of a conventional radio, largely 2 3

Equation (20.5) assumes all spectrum is available. If all of the spectrum is not available, the actual value of available channels must be used instead. Reference to the color version on the accompanying DVD is recommended.

Performance, Reliability, and Component Trades

365

due to the probability that a number of radios will need access to the spectrum simultaneously (5 to 12 channels). With small pool sizes that fit within a preselector, the probability of overload for both cognitive and noncognitive radios is identical, since there is no flexibility for the cognitive radio to avoid interference (5 to 10 channels). As the size of spectrum pool is increased (≥10 channels), benefit arises from two sources.4 .7

.5 .4 .3 .2 .1 0

m tru e ec ilur Sp Fa CR cess Ac

Probability of Failure

.6

CR

Ag Fa greg ilu ate re

CR Overload Probability

Non-CR Overload Probability

CR Overload Probability 5

10

15

20

!"#$%&'()''*+,--%./'01,2.,$.%' Number Channels Available

Figure 20.3 Relative availability for cognitive and noncognitive devices for some representative operating characteristics (duty cycle = 10%, IIP3 = −5 dBm).

As discussed in Chapter 11, the additional spectrum choices provide the radio with more flexibility in avoiding adjacent channel interference conditions, once more than one of the preselector alternatives has spectrum assignment candidates available.5 Increased spectrum also provides benefit in providing the statistical pool previously shown to be so critical. This illustrates that increased performance 4

5

The flat area of intermodulation performance is due to the filter bandwidth being larger than the extent of spectrum, and therefore any signal within this extent has a constant probability of inducing interference, regardless of the number of channels within the filter passband that are available for DSA operation. In this example, the link was determined to be unavailable whenever the intermodulation induced noise floor exceeded the natural one by more than 20 dB.

366

Quantitative Analysis of Cognitive Radio and Network Performance

of cognitive radio is dependent on the availability of suitable spectrum options. Without this available spectrum, there is no benefit, and possibly, regret from the implementation of DSA due to the lack of assured access to spectrum. However, when sufficient spectrum is available, the performance gains are significant and beyond those that can be readily assured even through additional link margin. The expected value of usage is less important than a statistically significant pool size. The specific duty cycle is not a fundamental driver in these relationships so long as the pool is statistically large and above the expected value of the demand. This is a strong argument that all users of cognitive radio would benefit when multiple uses are concatenated, creating options and statistical benefits to all contending users, since one large pool is much more desirable than many small ones. This is directly in contrast to the fragmented manner in which spectrum use is currently managed in the exclusive property rights model. 20.3.2

Decrease in Noise Floor Probability

Decreased noise floor inherently improves wireless link performance. The previous subsection addressed the availability or reliability implications; in this section, the opportunity to provide increased data rate is analyzed. The notional performance of a relatively heavily loaded transceiver (25% transmit duty cycle) depicted in Table 20.4 is used as a baseline. Table 20.4 Representative Baseline Transceiver Characteristics Symbol

Definition

Typical Value

Prcvanalog Prcvdig Pxmit PPA EffPA xmit TuningRange

Analog receiver power Digital energy usage in receiver (A to D and beyond) Transmitter section power, not including PA Power amplifier output power Efficiency of power amplifier Transmitter duty cycle Preselector tuning

200 mw 100 mw 150 mw 1w 30% 25% 1 octave

Although the actual operation of the channel and waveform modulation may not be at the ideal performance points of the Shannon bound, this analysis assumes that the derivative of the channel bit rate is consistent (i.e., that the relative change in throughput with a change in signal to noise ratio is approximately consistent). This

Performance, Reliability, and Component Trades

367

assumption essentially holds link margin and implementation efficiency constant through the range of rates in the neighborhood of the baseline case. Figure 20.4 illustrates the mean bandwidth improvement possible as a function of the cognitive radio adaptation capabilities, using the base case. In this example, the bandwidth increase is weighted by the probability of the intermodulation noise floor associated with the excess energy for bit rate increases. The occupied bandwidth is held constant. It is clear that the lower the filter performance (higher BW) of the baseline radio is, the greater the bit rate improvement that is possible and likely achievable through cognitive adaptation. The probability weighting means that, in most situations, the benefits of cognitive adaptation are not very significant. In some atypical (but not rare) situations, the front-end performance is the driver for the throughput of the device, and cognitive adaptation can make a significant contribution to the aggregate performance and reliability of the link. The benefits of cognitive adaptation are most significant for intrinsically low performance devices. This is another argument that cognitive radio should not be considered solely for sophisticated or high-performance applications. Application of cognitive radio technology should not be discounted, even in lowerend products, such as Wi-Fi and low-cost consumer networking products. 14

Increased Throughput

12 10 8 6 4 2 0

0

.1

.2

.3

.4

Filter Bandwidth Figure 20.4 Bandwidth improvement as a function of cognitive radio adaptation capabilities.

.5

368

20.3.3

Quantitative Analysis of Cognitive Radio and Network Performance

Increased Operating Period/Reduced Energy Storage Mass

The baseline values used above are typical of a small transceiver that might be used as a forwarder or trunking system radio in a mobile application. A general expression of total energy consumption of this radio is given by (20.6). In this representation, the analog receiver energy, which is strongly proportional to the LNA output third order intercept point (OIP3) (which scales with gain times IIP3) is a separate term from the digital processing (analog to digital conversion through baseband). The digital stages are generally insensitive to the analog stage characteristics, so long as appropriate gain to the A to D conversion is achieved.   PPA TotalEnergy = Prcvanalog + Prcvdig + xmit Pxmit + (20.6) Eff PA where all variables are defined per Table 20.4. Figure 20.5 illustrates the resulting operating life implications for this example case, but is also typical for a range of applications. For high duty cycle rates, the benefit from receiver energy reduction is not as significant (since transmit energy usage dominates), but as the duty cycle decreases, the benefits of receiver energy reduction increase and become very significant in the region below 35%. As a point of comparison, the Wi-Fi example cited previously has a 0.0067% utilization of the Wi-Fi device over a 24-hour period. In practice, current wireless LAN MAC layers rarely achieve over 50% transmit duty cycle, even when fully loaded.

20.4

FUNGIBILITY OF BENEFITS

The previous sections described the benefits of cognitive radio from the perspective of using the performance benefits of adaptation to specifically reduce resource requirements or purely to enhance performance by increasing availability and capacity, and reducing energy and noise. Alternatively, the benefits can be distributed among multiple objectives, and allocated across these four dimensions. Although previous sections have discussed these objectives as if they were orthogonal axis, they are surfaces on which a radio design can operate. As an example, the possible reduction in required IIP3 performance can be traded for increased reliability or probability of intermodulation induced noise floor. One possible allocation of benefits could simultaneously achieve a 99% reduction in front-end energy (20 dB), and reduce intermodulation noise (90% point) by 20 dB simultaneously for filters of 20% bandwidth, as shown in Figure 20.6. Note

Performance, Reliability, and Component Trades

369

Increase In Operating Life

12 10 8 6 4 2 30

FE

En

er 20 gy Re

du 10 cti on

(d B

)

0

100

80

60

40

uty mit D Trans

20

(% Cycle

0

)

Figure 20.5 Operating life extension from reduced front-end energy consumption for a range of duty cycles.

that cognitive radio benefits are not monotonic; front-ends with either high or low performance filters have greatly reduced benefits, and low performance filters provide few options for cognitive adaptation, and thus, greatly reduced aggregate benefit. This figure indicates that with very high performance filters (typically, passband under 5%), there is little mean energy savings due to reduction in the noise floor with these filters. The benefits can best be exploited profitably by the reduction in the required IIP3 level. As the filter bandwidth increases (decreasing performance), the choice between performance and component reduction becomes more complex. For example, with a 25% filter, up to 25-dB reduction in IMD3 noise (90% case) can be achieved, or the IIP3 can be reduced by 30 dB with neutral performance impact. Between these two extremes, a 20-dB reduction in IIP3 results in only a 6-dB reduction in benefits (to approximately 19-dB IMD3 noise reduction

Decrease (+) or Increase (-) in IMD3 Noise

370

Quantitative Analysis of Cognitive Radio and Network Performance

+60 +40 +20 0 -20 -40 -60

IIP -10 3 R -20 ed uc -30 tio -40 n

0

0.1

0.2

0.3

0.4

0.5

(BW) Filter Bandwidth

Figure 20.6 Trades between front-end linearity reduction, IMD3 induced noise and bandwidth.

at the 90% occurrence probability). Selection of the optimal point within the trade space is system- and application-specific.

20.5

CONCLUSIONS

Although the different aspects of environment selection would appear to be independent, there are opportunities to exploit these benefits across multiple elements of the hardware design, particularly in applications where there are significant reliability requirements. Additionally, cognitive radio can provide radio designers with unparalleled flexibility to adapt the design to reflect economic and other considerations. It can also allow regulators to take a more flexible approach to receiver characteristics, and provide designers with considerable flexibility to allow devices to implement their own strategy to operate in stressing environments, without regulation specifying specific hardware requirements [9].

References

371

EXERCISES 20.1

20.2

Determine the battery operating period savings that could be achieved through the use of a cognitive radio through IIP3 reduction. Assume the Chicago environment, and that transmit energy was negligible. The noncognitive radio has a −5 dBm IIP3. Perform the same problem as above, except using New York Day 1, and an existing radio with a +5 dBm IIP3.

20.3

Perform the same problem as above, except using NRAO data, and an existing radio with a +5 dBm IIP3. Compare the benefits of cognitive radio with other examples in the book, and the results of the two prior problems.

20.4

A communications system operates at an acceptable reliability. It has a 1 dBm IIP3, and a 1 watt transmitter. How much could the transmit power be reduced if the same radio was cognitive? Clearly state any assumptions you were required to make in order to solve this problem.

20.5

Using the performance relationships from this chapter, describe an algorithm to examine proposed spectrum bids from a spectrum broker and to select operating modes that reflect the relative instantaneous cost of the spectrum.

20.6

A communications systems is operating effectively at a fixed communications rate of 1 Mb/second in Chicago. The same radio will now be operated in a DSA mode. It has an IIP3 of +5 dBm. What data rate can be guaranteed to a 99.9% reliability? What data rate can be guaranteed to a 90% confidence?

References [1] M. McHenry, E. Livsics, A. Leu, D. McCloskey, V. Suri, D. Padrick, and A. Poddar, “Tuner utilization and feasibility (TUF) study final report,” Shared Spectrum Company, available from DTIC, or the Company, Tech. Rep., Dec. 9, 2005. [2] P. F. Marshall, “Sensor networking: radio and networking technology for sensor applications (invited paper),” Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, ser. Society

372

References

of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, vol. 5796, May 2005, pp. 235–245. [3] G. R. Faulhaber and D. J. Farber, Spectrum Management: Property Rights, Markets, and the Commons. Working Paper 02-12: AEI-Brookings Joint Center for Regulatory Studies, Dec. 2002. [4] United States Federal Communications Commission, Spectrum Policy Task Force Report. Docket No. 02-135, Nov. 2002.

ET

[5] G. Isiklar and A. Bener, “Brokering and pricing architecture over cognitive radio wireless networks,” 5th IEEE Consumer Communications and Networking Conference, Jan. 2008, pp. 1004–1008. [6] O. Ileri and J. Zander, “Broker coordination in demand responsive dynamic spectrum access settings,” IEEE International Conference on Communications, June 2009, pp. 1–6. [7] J. Acharya and R. Yates, “Service provider competition and pricing for dynamic spectrum allocation,” International Conference on Game Theory for Networks, May 2009, pp. 190–198. [8] J. Bae, E. Beigman, R. Berry, H. M.L., H. Shen, R. Vohra, and H. Zhou, “Spectrum markets for wireless services,” 3rd IEEE Symposium on New Frontiers in Dynamic Spectrum Access Networks, Oct. 2008, pp. 1–10. [9] B. C. Klopfenstein and D. Sedman, “Technical standards and the marketplace: the case of AM stereo,” Journal of Broadcasting and Electronic Media 171 (1990), vol. 34, no. 2, pp. 171–194, Spring 1990.

Chapter 21 Large-Scale System Experiments and Demonstrations 21.1

OVERVIEW OF EXPERIMENTATION AND DEMONSTRATION

This chapter will summarize some significant and large-scale cognitive radio and adaptive networking experiments that are important to the consideration of cognitive radio deployment. Many smaller-scale experiments were described in Chapters 3 and 4. These were primarily engineering demonstrations to the cognitive radio community itself. This chapter will focus on the experiments and demonstrations that were focused on demonstrating system value through cognitive radio. The audience for these experiments needs to be the larger community that will have to provide research funding, venture funding, and policy initiatives, and to support the productization of the technology based on tangible evidence of meaningful capabilities that could be provided. While it is relatively straightforward to perform experiments at the link level, meaningful cognitive radio experimentation and demonstration is dependent on the interaction of a large number of radios and devices, and therefore experiments are complex, require extensive spectrum, extended regions over which to be deployed, as well as a large number of assets. Using current wireless technology, the devices themselves are much more expensive than consumer electronics, such as the Wi-Fi devices that are used in any networking experiments. The base from which to draw experimental validation of cognitive radio is therefore quite small, and primarily limited to the following sources: 373

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Quantitative Analysis of Cognitive Radio and Network Performance

TV White Space Sensing: Previously, Chapter 4 discussed the successful campaign to demonstrate to the FCC through laboratory trials that sensing of TV signals was achievable to the detection threshold mandated. With this laboratory success in sensing success, the next stage of this technology needs to develop field experiments that demonstrate that realistic field deployments of white space devices have the detection success that was indicated in the laboratory testing [1, 2], and that with the constraints imposed by regulation that a reasonable number of these devices can share the spectrum with each other and create a meaningful capability. The important transition is from demonstrating engineering characteristics to engineers to demonstrating system capabilities to users and potential advocates. Academic Research and DYSPAN: Academic researchers have been highly successful in demonstrating major portions of the technology for a cognitive radio, and have reported considerable progress towards demonstrating small numbers of complete systems. The IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN) conference has had a highly popular demonstration track, and some of the reports on these experiments are also included in Chapter 4 [3–5]. Additionally, a number of academic institutions have established testbed environments in which to experiment with cognitive radios, including Rutgers [6] and UC, Berkeley. DARPA: A number of experiments have been conducted by the U.S. Department of Defense’s (DoD) Defense Advanced Research Projects Agency (DARPA) arm. Many of these experiments have been described in the public literature. The programs included in this discussion include: neXt Generation Communications (XG): The XG Program demonstrated noninterfering DSA operation of a number of networks and incumbent radios. Wireless Network after Next (WNaN): The WNaN program is intended to demonstrate many of the principles described in this book, including multitransceiver operation, use of low performance front-ends, linearity management, tunable filters, dynamic network scaling, and contentbased networking. Disruption Tolerant Networking: The delay and disruption tolerant networking (DTN) demonstrated the benefits of hop by transport of contents, and the use of adaptive caching and persistence within wireless networks.

Large-Scale System Experiments and Demonstrations

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The rest of this chapter will focus on the large-scale field tests of cognitive radio technology reported by the DARPA programs. 21.2 21.2.1

DARPA NEXT GENERATION (XG) PROGRAM XG Program Overview

The DARPA neXt Generation (XG) communications program was focused on the development of an effective DSA demonstration and capability. The program was intended to evaluate three fundamental principles: 1. That DSA radios could be designed so that they did not interfere with viable links for other noncooperative users of the spectrum, presumably primary users. This principle was broadly known as “Do No Harm.” 2. That DSA radios had a positive benefit after all of the overhead costs and processing resources were included in the determination. This was the “Add Value” principle. 3. That DSA devices could be developed that would provide equivalent reliability and service despite the additional complexity of the interference avoidance requirements. This was the “DSA Works” principle demonstration. 21.2.2

XG Program Field Trials

XG program demonstrations were held numerous times in 2006 through 2008, and met the criteria described previously. The basic principles were shown in 2006 by deploying a field of conventionally deconflicted wireless links, including typical military, public safety, and civilian point-to-point links, deployed over a range of several kilometers. These radios occupied all of the spectrum available for the experiment. To demonstrate that spectrum was not available, a set of four mobile radio pairs was introduced, and it was shown that no matter how these were assigned or located, some cases of interference were inevitable and unavoidable when the XG mode was not enabled. When the XG mode was enabled, the same set of radios could coexist; no matter how they were distributed throughout the operating region, no interference to the nonadaptive (primary) users was present. Where no spectrum was available, the XG radios were shown to avoid interference even if they had to cease operation.

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Quantitative Analysis of Cognitive Radio and Network Performance

EPLRS XG5

XG3

XG6 PRC-117 Micro-Lite

XG4

XG drive path XG2

XG1

Night Vision Observation Building

Jammer PSC-5 ICOM

Figure 21.1 XG field experiment layout at Fort A. P. Hill (from Marshall [7]).

The initial position of the radios in the 2006 XG demonstration site layout are shown in Figure 21.1.1 The dotted line represents the drive path that the XG radios traveled to encounter the different interference conditions. The radio networks shown on the field are: EPLRS: Enhanced Position Reporting System (EPLRS) is a high-power, military networking radio using TDMA slotted MAC layer. PRC-117: Military digital voice radio. Micro-Lite: Low-power version of the EPLRS radio. ICOM: Commercial public safety radio that is in use for a variety of applications. 1

Color images of all photos in this chapter are on the accompanying DVD.

Large-Scale System Experiments and Demonstrations

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PSC-5: Military voice radio. Operated on the same channels as the XG radios were attempting to share. XG1-XG-6: Statically configured XG radio pairs. These operated as one WiMax base station, plus one or more subscriber units, each in a separate vehicle. The initial configuration of nodes could not be all active without causing significant interference to many of the nodes due to the proximity of XG nodes to noncooperative receivers that were within the interference radius. This experimental configuration was shown to demonstrate that the XG mode was responsible for the interference-free characteristics that resulted when the XG mode was enabled. The same measurements were then made with the XG nodes in motion in the vicinity of the uncooperative nodes. XG nodes were shown to avoid all interference to the legacy nodes, even though at times the conservative spectrum policy forced them to shut down to ensure noninterference. Perhaps the best measure of the results of the XG program is to examine the spectrum occupancy levels that were achieved in the field trials. This is reported by McHenry [8], and shown in Figure 21.2. Note that the XG avoided interference even when all of the frequencies were in use. In the dense areas of the trial area, the spectrum occupancy approaches 100%, which explains why the XG algorithms had to occasionally shut down the XG nodes, since there was no spectrum available for XG to use without risk of interference to users protected by policy. Another important metric from this experiment was the link and network establishment time, which was used in Chapter 14 to derive the worst-case values of IDSA . The distribution of the abandonment time is a function of the number of XG network nodes. Experimental results are provided by McHenry [9], and shown in Figure 21.3. 21.2.3

XG Radio Design

The XG radio design was built to maximize its flexibility and provide an adaptive platform for a range of DSA experiments. It was not initially intended as a production radio, and no attempt was made to minimize its cost or perform systems analysis to balance the performance of the individual components. Pictures of some of the major assemblies are provided in Figure 21.4. Clearly, this radio was not built for production, but solely as a flexible platform for experimentation.

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Quantitative Analysis of Cognitive Radio and Network Performance

Spectrum Occupancy! 0%! 17%! 33%! 50%! 67%! 83%!

c IEEE 2008, Figure 21.2 Achieved XG spectrum occupancy levels (from McHenry [8] used by permission).

21.3 21.3.1

DARPA WIRELESS NETWORK AFTER NEXT (WNAN) WNaN Objectives

The Defense Advanced Research Projects Agency (DARPA) Wireless Network after Next (WNaN) program is currently developing the network and radio technologies to enable low-cost, affordable, and expendable devices to be used in highly demanding military communications missions and to provide a new generation of self-organizing network capabilities. These technologies not only provide packet routing and delivery, but integrate content management into the network to improve its ability to adapt to user information needs. It also positions content appropriately

12:15 PM

Page 55

Large-Scale System Experiments and Demonstrations

0.6

Additional nodes take more time because of messaging delays

0.5 0.4

500 ms goal

0.3 2 nodes 3 nodes 4 nodes 5 nodes 6 nodes

0.2 0.1 0

0

50

100

150

200

250 300 Time (ms)

350

400

450

500

■ Figure 3. Network re-establishment time. Figure 21.3

c Achieved DSA XG spectrum abandonment time (from McHenry [9]) IEEE 2007, used by permission. XG link uptime analysis

15

1B the network to create a completely self-sufficient tactical Scenario and dynamically within Scenario 2B edge capability. The fundamental premise of this program is that wireless networks face rare most cases, XG radios but highly stressing environmentsForwhose mitigation is a significant driver for their maintained 90+% cost. By using evolving cognitive connectivity radio technology to adapt the device’s selection of operating 10 environment, WNaN reduces the component performance requirements on individual components to the point where the mission needs can be met with the performance levels provided by high volume and integration level commodity, commercial components (for example CMOS RFICs). Outlier that may radio can be considered to arise in several ways: The benefits ofcases a cognitive be attributed to improving the computer performance of similar equipment, reducing performance requirehardware or software ments of5 equipment crasheswhile not sacrificing capability, or through a mix of both. Until recently, the affordability argument has received scant attention. The explicit goal of the DARPA WNaN program is demonstrating that a cognitive network can both

Link uptime (%)

■ Figure 4. Summary of link uptime.

95–100

90–95

85–90

80–85

75–80

70–75

0 65–70

UPTIME

ined as the percentage of time ns its connection to all nodes in mally, this should be 100 perd because of several factors. not possible when all nodes eachable. If an XG node is nge, no amount of networkan overcome the issue of al strength. also not possible if there is nnel for communication. ay not be possible when the

Three or more nodes take longer than two nodes because base station has a delay after change frequency command to allow subscriber queues to empty

0.7

60–65

ORKS: LINK

0.8

55–60

project goal was that no precies are required for network o systems use beacons on preies to get spatial and temporal sary for network startup. Netocols call for meeting on prees to get download information y lists, time-division multiple ot times, code-division multiple odes for fast acquisitions, and networks use this to confine etwork acquisition intelligence s so that cell phones produced n be made cheaply. llenge was to develop infrasorks that did not depend on s or fixed frequency bands. To e, every XG node is capable of o prior information regarding ons, cell size, startup frequene from legacy radio systems. re designed to not rely on precies for network startup other nels used in the test. The only equencies were used for the at no additional frequencies were available at Fort A. P. laboratory show that the XG e with > 50 available channels.

“Kink” in the two-node case is caused when the base station detects non-cooperative and then has a delay after change frequency command to allow subscriber queues to empty

0.9

50–55

PRE-ASSIGNED FREQUENCIES

379

Channel re-establishment time — cumulative distribution function 2006-09-25, lab test, re-establish network of X nodes, six available channels

1

Probability of occurence

hannel. The XG network abanel and then chose one of the annels to re-establish connecblishment time was measured rials. The number of nodes per rom two to six. The result is The channel re-establishment n 150 ms in all test conditions. PA XG program goals.

Number of occurrences

7

380

Quantitative Analysis of Cognitive Radio and Network Performance

Display showing XG operational state Rockwell Sensor

RF Power Amp

RF Enclosure

GPP with 802.16 modem GPP with XG algorithms

225-600 MHz RF Transceiver (located under shelf)

Figure 21.4 The 2006 XG experiment radio (from Marshall [7]).

achieve high levels of performance and can do so at lower cost due to effective adaptation methodologies, and therefore reduce the need for high-cost and highenergy consumption components in the device. Additionally, the program would address many of the network scaling issues associated with self-forming MANET and peer-to-peer networks. It would apply the technology developed in the DARPA XG program for DSA. One of the experimental nodes is shown in Figure 21.5. One example of the approach of cognitive radio as an enabler of lower cost hardware is in mitigating the effects of front-end overload. Even in relatively benign civil environments, measurements shown in the earlier chapters show urban areas have energy levels above −10 dBm in 5% of the narrow frequency bands. When applied to a receiver front-end with a −5-dBm input third-order intercept point, this would result in noise floor elevation of over 80 dB above the typical thermal noise floor. WNaN instead will select among the available spectrum bands and frequencies to not only select open frequencies, but those whose environment are within the capability of the device. The WNaN program objectives were described in 11 theses for the objective wireless network, and were previously shown in Table 18.1.

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These principles provide the structure for incremental development and evaluation of the WNaN capability through several stages of the program. Unlike some prior DARPA programs in network development, this one will provide opportunities for experimentation and potential user evaluation at each stage, both to judge progress and to prioritize the effort in the next phase.

Figure 21.5 Current WNaN node design (from DARPA).

21.3.2

Notional Hardware Concept and Design

One objective of the WNaN program is to reduce the dependency of network technology on the capability of the underlying hardware layer. Today, the network support requirements are typically flowed to the hardware layer. In WNaN, the hardware layer informs the network of its capability, and then the network adjusts its operations to best exploit the capabilities that are provided. The hardware used for WNaN development is intended to be representative of the hardware that could be periodically reprocured, as the underlying commercial components achieve higher levels of performance, integration, and increased affordability. The initial WNaN radio has four independent transceivers, covers 900 MHz to 6 GHz, has high quality front-end filters to enable the DSA functions to identify and utilize low energy preselector bands, and uses commercial quality CMOS radio frequency integrated circuits (RFICs) for transceiver functionality. This platform is intended to host not only DSA functionality, but also cognitive topology and content management integrated with the logic for radio and network operation. The heart of the radio is the four wideband CMOS RFICs, currently provided by ASIC-Ahead, but likely soon to have a number of competitive sources. It includes all commercial components with the exception of the front-end preselector filters, which have the requirement to tune from 900 MHz to 6 GHz, and thus do not match available low-cost commercial parts. The performance of these filters is critical to the flexibility of the radio in selecting operating environments, and is a key program

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Quantitative Analysis of Cognitive Radio and Network Performance

objective. Most other parts are commercial digital components, and will presumably evolve as better, or lower cost, as parts become available. The inclusion of MIMO operation is integral to robust operation in dense urban environments. Whereas the commercial appeal of MIMO has been in providing maximal throughput, in WNaN the objective is to enhance reliability and performance in situations where the capability of the network would otherwise be marginal or unacceptable. As such, it maximizes the minimum performance rather than maximizing the maximum performance, as in the Wi-Fi MIMO 802.11n mode. This design is not itself an objective of the program. In fact, a measure of the programs success would be the ability to rapidly evolve new designs that can exploit the latest commercial components, providing future generations (perhaps three years per generation, as in cell phones or laptops) of WNaN devices enhanced performance and affordability exploited through the dynamic network and content management layers. WNaN radios are intended to be so inexpensive that they are expenses, not investments.

21.4

DELAY AND DISRUPTION TOLERANCE NETWORKING

One of the inherent characteristics of any communications system operating close to the ground is the inevitable loss of connectivity caused by ground obstructions and other effects of low antenna height. The occasional loss of connectivity has a disproportionate effect on transport layer operation, particularly when there are multiple unreliable hops in the path. In the extreme case, IP-based networks are incapable of delivering content when the two endpoints are not present on the network simultaneously, even if there are reliable opportunities available through intermediate nodes (albeit at different times). The DARPA Disruption Tolerant Networking Program is an application of the Delay Tolerant Networking (DTN) Research Group (DTNRG) architectural framework, as provided by Cerf [11]. One of the significant DTN experiments was reported by Parikh [12] and Scott [13]. Figure 21.6 and Figure 21.7 are examples of the impact of introducing DTN services to unreliable links. Figure 21.6 illustrates the bandwidth usage of a blue force tracker client operating over an unreliable link. The lack of reliability forces the server to use bandwidth to maintain synchronization of the client and server, and has the additional burden of end-to-end transmission of any lost packets. Figure 21.7 illustrates the same client and server applications, except modified to use the end-to-end DTN services instead of the TCP transport layer. The ability of DTN to assure that the server’s perception of client state is accurate at the time

Large-Scale System Experiments and Demonstrations

383

of content delivery, plus the hop-to-hop (versus end-to-end) management of the transfer creates approximately a 75% reduction in bandwidth usage. *+% -%./0$'1'$(%

$!#0%&'()%

,*%

!"#$%&'()%)*+,-./)*,*+% !"#$%&'$(')%

+%

Figure 21.6 Typical bandwidth consumption without DTN (Marshall [10]).

*+% -%./0$'1'$(%

.!"/#$%&'#

,*%

!"!#$%&'#'()*+,-'(*()# !"#$%&'$(')%

+%

Figure 21.7 Typical bandwidth consumption with DTN (from Marshall [10]).

21.4.1

DTN as a Vehicle for Content-Based Access

A fundamental premise of current networking practice has been to abstract the network and applications layer so that their technology, behavior, and operation is independent and isolated. Certainly, this abstraction has been one of the keys to the growth of the Internet, as technologies such as the World Wide Web have grown asynchronously with the underlying networking technologies. However, wireless is

384

Quantitative Analysis of Cognitive Radio and Network Performance

an environment where bandwidth is scarce and costly in terms of energy, spectrum, and equipment. Interaction with DTNs is performed using a bundle metaphor. A bundle is an integral grouping of metadata and data. Unlike a packet, it provides a context for any node that processes it. As a first step in the process of developing a content-based network, the DARPA DTN program is experimenting with the use of caches to maintain opportunistic copies of content at all nodes that encounter the bundle. These nodes could be sources, recipients, or intermediate routers. They retain content as it passes through them. In the case of wireless nodes, the bundle may not even be intended for the node; it might have been addressed to another network member but is retained if the transmission is overheard. DTN provides a network information metaphor in which access to network content can be performed based on the description of content, rather than specification of end nodes at which it is located. Distributed, peer content caching is one technology to reduce the edge’s dependence of the core, reduce the backhaul bandwidth needs, and evolve to peer-to-peer architectures that are rich in range of content. The bundle enables separate encryption for the bundle as a whole, the metadata, and the payload to provide authentication and confidentiality. Nodes can provide caching or servertype services for content they cannot actually access, providing a black side cache or server inherent in the operation of the CBN. DTN caching and late binding were recently tested in a variety of wireless networking scenarios. The ability to cache content, defer address resolution and maintain connectivity in disrupted environments was shown to improve wireless performance by factors of five to ten in typical military wireless networks. DTN concepts have been applied individually for many years; what is unique is their integration into a single, pervasive network service.

21.5

CONCLUSION

A number of programs are establishing experimental evidence for the technical and business justification for the transition of cognitive radio from research to deployment. Suitable system-level results are the key to transitioning adaptive radios from an adjunct role in current wireless architectures and services to becoming an enabler of operation in new environments and new and exciting wireless capabilities that otherwise could not be provided. This transition will be dependent on system-level demonstrations of the enhanced capability, utility, or affordability of these solutions.

References

385

Cognitive radio will need to show more than engineering benefits to be embraced as an investment opportunity for developers and users of wireless systems. System experiments are more complex and less reproducible than link-focused experiments, so it is critical that the maximum amount of information be obtained from each of these expensive experiments. EXERCISES 21.1

Design an experiment to validate the concepts developed in Chapter 11 (front-end energy management) to verify that a set of nodes could locate frequencies that would be compatible with their front-end characteristics.

21.2

Design an experiment to validate the concepts developed in Chapter 12 (selection of channels) to verify that a set of nodes could locate frequencies that would have the minimal noise floor.

21.3

Design an experiment to validate the concepts developed in Chapters 13 and 14 (interference tolerance) to verify that a set of networks can operate effectively even when they are causing link layer interference between them, and determine the maximum density that can be achieved until the aggregate throughput is diminished.

21.4

Design an experiment to validate the concepts developed in Chapter 15 (interference footprints) to verify that use of propagation awareness can minimize the footprint of devices so as to maximize throughput.

21.5

Design an experiment to validate the concepts developed in Chapter 18 (application services) to verify that active and aware management of content location can improve cognitive radio performance.

References [1] FCC Office of Engineering and Technology, Initial Evaluation of the Performance of Prototype TV-Band White Space Devices, July 2007. [2] FCC Office of Engineering and Technology, Evaluation of the Performance of Prototype TV-Band White Space Devices Phase II, OET Report: FCC/OET 08-TR-1005, Oct. 15, 2008. [3] Trinity College, Dublin, Centre for Telecommunications Value Chain Research. Multiple videos of demonstration projects, http://www.youtube.com/user/EmergingNetworks.

386

References

[4] D. Taubenheim, W. Chiou, N. Correal, P. Gorday, S. Kyperountas, S. Machan, M. Pham, Q. Shi, E. Callaway, and R. Rachwalski, “Implementing an experimental cognitive radio system for DYSPAN,” Global Telecommunications Conference, 2007. GLOBECOM ’07. IEEE, Nov. 2007, pp. 4040–4044. [5] P. Amini, E. Azarnasab, S. Akoum, and B. Farhang-Boroujeny, “An experimental cognitive radio for first responders,” 3rd IEEE Symposium on New Frontiers in Dynamic Spectrum Access Networks, Oct. 2008, pp. 1–6. [6] D. Raychaudhuri, N. B. Mandayam, J. B. Evans, B. J. Ewy, S. Seshan, and P. Steenkiste, “Cognet — an architectural foundation for experimental cognitive radio networks within the future Internet,” First International Workshop on Mobility in the Evolving Internet Architecture, 2006. [7] P. F. Marshall, “DARPA progress in spectrally adaptive radio development,” Software Defined Radio Forum Technical Conference, 2006. [8] M. McHenry, K. Steadman, A. Leu, and E. Melick, “XG DSA radio system,” 3rd IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, Oct. 2008. [9] M. McHenry, E. Livsics, N. Thao, and N. Majumdar, “XG dynamic spectrum access field test results,” IEEE Communications Magazine, vol. 45, no. 6, pp. 51–57, June 2007. [10] P. F. Marshall, “Progress towards affordable, dense and content focused tactical edge networks,” IEEE Military Communications Conference (MILCOM), Nov. 2008, pp. 1–7. [11] V. Cerf, S. Burleigh, A. Hooke, L. Torgerson, R. Durst, K. Scott, K. Fall, and H. Weiss, DelayTolerant Network Architecture, IETF RFC 4838. Internet Engineering Task Force, Apr. 2007. [12] S. Parikh and R. Durst, “Disruption tolerant networking for Marine Corps CONDOR,” Proceedings of the 2005 Military Communications Conference, Atlantic City, NJ, Oct.17–20, 2005. [13] K. Scott, “Disruption tolerant networking proxies for on-the-move tactical networks,” Proceedings of the 2005 Military Communications Conference, Atlantic City, NJ, Oct. 17–20, 2005.

Chapter 22 Desirable Cognitive Radio Implementation Technology Developments 22.1

ENABLING TECHNOLOGY AREAS FOR COGNITIVE RADIO

This chapter will review some technology advances that have the potential to fundamentally change the cognitive radio field, by shifting the relative capabilities and economics of the component segments of wireless devices. Evolutionary or revolutionary improvements in these technology areas would have significant benefits to the viability and performance of cognitive radios. Conversely, other technologies could negate the necessity for cognitive radio by providing lower cost and risk alternatives to the complex reasoning and sensing architectures now envisioned as necessary for cognitive radio operation. Although this chapter is focused on the implementation of cognitive radios, the constraints of device fabrication will also be addressed in the context of conventional radio performance and its impact on potential regulatory treatment of receiver quality. The technology areas to be discussed are: • Low-cost, tunable filters with at least an octave of coverage, in order that a DSA-based radio can provide high confidence of locating usable frequencies; • Flexible and reasonably affordable RF processing facilities, such as CMOS RFICs, that can provide the flexibility that cognitive radio requires in order to significantly improve the performance of the fixed mode and spectrum alternatives; • Algorithms and processing resources for evaluating policies, to learn and to manipulate large numbers of operating alternatives. 387

388

Quantitative Analysis of Cognitive Radio and Network Performance

22.2

FRONT-END FILTERS

Chapter 11 demonstrated the criticality of filter performance to operation of low performance cognitive radios in dense spectrum. It showed that cognitive radios can produce significant benefits if the front-end filters have a range of independent filter choices. Unfortunately, most existing radios use RF filters designed for fixed frequency applications such as cell services, so the technology base development has not focused on low cost, high performance, wide-range, tunable filters. In this discussion, we will consider three significant figures of merit for radio front-end filters: Tuning Range: The tuning range of the filter. There are two considerations. The first is the range of tuning that is possible, as measured in percentage or octaves of the center frequency, and the second is specific frequency value, since the physical characteristics of many of the filter technologies do not scale with frequency. Thus, a design approach that might be effective at 300 MHz may not be usable at 3 GHz, and vise versa. Selectivity: Selectivity measures how narrow the filter’s response is. Typically, the measure is for the 3 dB (half power) loss, on either side of the center frequency. In general, so long as the filter passband is in excess of the signal bandwidth (a condition true for most cases), the more selective a tunable filter is, the more effective it will be in cognitive radio, if all other considerations are equal. More filter selectivity provides more options to avoid high-energy front-end bands and the ability to utilize spectrum in closer proximity to strong adjacent signals. Selectivity considerations include both the low-loss portion of the passband (such as the 3-dB points in the passband) and the amount of spectrum between this point and where there is a high degree of attenuation, a region referred to as the filter skirt. Insertion Loss: Insertion loss is the signal attenuation at the center of the filter passband. Historically, this has been a fundamental measure of filter performance and acceptability, since any signal attenuation directly reduces the receiver sensitivity. However, in many communications environments, the actual noise floor is not defined by the internal noise in the receiver, but by noise from the environment. In this case, the signal to noise ratio is established by the environment, and is insensitive to the noise level of the receiver front-end.1 1

This is one reason why an HF signal can often be acceptably received by an antenna as unsophisticated as a paper clip, since the atmospheric noise at several megahertz is much higher than the front-end noise. Conversely, at UHF, the front-end noise often dominates, and therefore the frontend noise often defines the signal to noise ratio, and antenna performance is critical.

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Environmental noise generally dominates in the HF bands where atmospheric noise is high, but this condition is often present in many of the VHF and UHF communications bands, where there is so much background noise from other users, harmonics, amplifier distortion, and receiver intermodulation that the loss of sensitivity may have only a minimal effect on the operation of wireless devices. As spectrum usage becomes denser, the noise floor is increasingly likely to be dominated by these man-made effects, and insertion loss should become less of a compelling consideration. These are not the sole metrics of filter technology. Other ones that must be considered include the filter ripple and the group delay spread.2 There are several design approaches to providing tunable filters in receiver front-ends. These are driven by performance, volume, cost, and frequency considerations. Many of the filter technologies are not applicable across wide ranges of frequencies, so the techniques discussed here are not applicable to all applications, as is true for the intermediate frequency and baseband elements of the receiver. Some commonly applied design approaches to tunable filters include: Selectable Fixed Filters: The simplest tunable filter is a set of fixed frequency filters, one of which can be selected for each band of operation. This is typically what is provided in low-cost consumer equipment such as cell phones. It is practical if the number of filter options is relatively low, since the switching structure becomes more complex at an order of the square of the number of filters. In low-cost equipment, the filters would typically be surface acoustic wave (SAW) filters, which are low-cost, fixed frequency silicon resonators, or bulk acoustic wave (BAW) or ceramic filters, which have desirable features but higher cost. The very selectivity of these filters is somewhat of a disadvantage, as large switching networks and many filters would be required for bandwidths approaching an octave. Varactor Tuned: Varactor tuned filters consist of a fixed inductive element and one or more variable capacitors. Varactors are voltage variable capacitors, and have a capacitance that varies with the voltage applied across the device. A variable DC bias voltage is applied to the device to vary its capacitance, and thus, the frequency of the filter. The tuning range of these filters is limited due to the fixed inductance. A variation on this design is a distributed variable dielectric. The fixed inductance element limits the frequency range due to the 2

The difference in signal delay across the passband of the filter. If this delay is significant in terms of the symbol duration, then it will impact the ability to process the symbol, similar to the effect of multipath intersymbol interference.

390

Quantitative Analysis of Cognitive Radio and Network Performance

minimum capacitance of the varactors, the stray capacitance of the circuit, and the impedance matching to the antenna. An example of this type of filter is found in Dalal [1], describing a filter with a tuning range of 60%. There are other technologies that are the subject of more-experimental or limited operational use. These include magnetically tuned, superconducting, and yttrium iron garnet (YIG) tuned. One of the most promising future technologies for filter implementation is microelectromechanical systems (MEMS). MEMS are typically silicon devices that have an internal electrostatic mechanism to physically move parts themselves, such as a switch, motor, pump, or transducer. Initially MEMS technology was applied to provide variable resonance structures that would physically change dimensions to vary the resonant frequency. These designs have not yet shown the necessary stability and manufacturability to become practical for widespread use, but simpler designs that utilize MEMS only as switches or capacitors have shown promise. In these designs, the MEMS switch or MEMS body itself acts as a two-state capacitor, similar to a varactor. This solves some of the inherent issues with varactor designs, as the device does not have a conductive path across the circuit (which lowers the possible Q in a varactor circuit), and its capacitance value is not impacted by the signal, avoiding one of the causes of intermodulation in a varactor design. As examples, Rabeiz has considerable detail on these designs [2], and a typical tunable filter design is provided by Brank [3]. Another promising line of research is in the use of metamaterials. A great range of these materials is under investigation, but at least some promise the ability to change the RF propagation characteristics in ways that would make tunable filters possible. At this time, the materials have limited bandwidth, but this limitation could potentially be overcome. Interesting work has integrated MEMS and metamaterial designs. If any of these future candidates are successful, it will solve one of the fundamental limitations of the widespread application of cognitive and software defined radios.

22.3

RF CMOS

Most technology trends result in higher performance devices. The increasing capability of CMOS fabrication of RF devices has an opposite effect: it can produce typically lower performance parts at radically lower cost due to sharing the fabrication process with digital components, and reduces the assembly cost by providing RF

Desirable Cognitive Radio Implementation Technology Developments

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components integrated and interconnected at the chip level rather than through manufactured “board level” interconnects. This transition was first apparent in products that integrated Wi-Fi capability into microprocessors, but now has become more prevalent in more applications. A decade ago, a wireless device would typically be assembled from either discrete components (such as transistors, coils, or inductors) or as an integrated component, using processes specific to RF applications such as silicon germanium (SiGe) or gallium arsenide (GaAs). Highly integrated, high-performance RF devices required unique processes, such as in monolithic microwave integrated circuits (MMIC), that do not have high volume utilization, require expensive substrate materials, and are somewhat difficult to integrate with traditional digital logic. The (typically) lower performance of RF CMOS is due to several factors, including: 1. The silicon process is generally optimized for digital logic that has only two states (zero and one), and is therefore not optimal for linear processes that may take on an essentially infinite number of unique values, such as an RF signal. 2. High-density digital circuits have limited substrate area for each transistor junction. The possible current through the junction is therefore limited, and likewise the maximum current it can conduct linearly, in relation to the current corresponding to the device noise. This is one of the constraints on the dynamic range of the analog portion of RFIC implementations. 3. The behavior of silicon-based circuits is highly sensitive to temperature, so ensuring stable gain and circuit operation is difficult across a wide range of operating temperatures. 4. The elements of the circuits are in close proximity to each other (typically separated by a distance range from 20 to 130 nm). There are a number of sources of signal and noise energy that can be inadvertently coupled into sensitive receiver circuits, or that can produce feedback that leads to unstable circuit operation. The impact of many of these effects is likely to become worse as technology is developed to increase the density of CMOS circuits. Higher density circuits can create more local heating, more coupling of spur signals into RF paths, and reduces the junction size, limiting the maximum current that can be tolerated and thus constraining the dynamic range that can be achieved. Although reduced RF performance might appear to be an obstacle to the use of these devices within cognitive radio, in fact the opposite may be the case. Chapter

392

Quantitative Analysis of Cognitive Radio and Network Performance

11 illustrated that the use of cognitive radio can mitigate some of these limitations in spur signals and front-end linearity. Another cognitive radio consideration is that spectrum regulators have generally been reluctant to establish standards for receiver performance. The (perhaps implicit) assumption has been that performance would inherently improve with technology, and therefore, both market and technology forces were more appropriate mechanisms to establish acceptable receiver performance levels. This approach may come to conflict with the digital era in several ways: 1. Consumers assume that technology will become more effective. As shown, this assumption is not always valid when the transition from discrete fabrication to integrated IC is considered. 2. Consumers are becoming used to the commodity nature of digital systems, so the idea that analog functions are not just equivalent commodities is the opposite of their experience. For example, digital high definition televisions (HDTVs) are advertised based on pixels and lumens, not noise temperature, intermodulation, overload performance, or image response. 3. The existence of large numbers of poor receivers creates a burden on other users of the spectrum. For example, the subtle nature of out-of-band signal response is lost when a politically influenced process is impacted by a large constituent community, as discussed in Chapter 3. Even the poor quality of a receiver can establish a perceived “right” to be able to utilize legacy equipment, and force other spectrum users to constrain their use of the spectrum to the limitations of the poorest equipment. It is likely that the full success of cognitive radio will be dependent on the availability of highly capable and low cost transceivers. The most likely technology to make these available is through the CMOS processes that produce both the digital and analog circuits that are prevalent in most consumer products. Although most products in this technology are focused on specific applications in cellular and WiFi, there has been some product interest in more generalized RFICs that are suitable for high-performance cognitive radios. Attributes of a cognitive radio applicable RFIC solution would include: Coverage: A cognitive radio RFIC should have sufficient tuning range to offer spectrum sharing opportunities across a wide range of potential bands. Ideally, it should also offer sufficient tuning range to be able to make use of varying propagation characteristics to balance range, bandwidth, and density. Signal Processing: The signal processing chain should be flexible to accommodate a range of signaling mechanisms. Many specialized chips have integrated the

Desirable Cognitive Radio Implementation Technology Developments

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operation of the selected waveforms into the operation of the RF circuits, and therefore cannot readily implement other forms of channel control, duplex operation, modulation, EDAC, and so forth. Air Interface: The digital signal processing should not be tied to a particular modulation, waveform, or MAC layer standards. For example, many commercial Wi-Fi chips are of limited use for research even in the ISM bands, because the chips are “hard-wired” to the IEEE 802.11a, b, g, and/or n standards. Several manufacturers have prototype general purpose RFICs that appear suitable for cognitive radio. The DARPA WNaN project used a chip design derived from the ASIC AHead 1001 [4]. Motorola provided sample RFICs to researchers at Virginia Tech, who report the experience of fabricating a transceiver system [5, 6]. At the time of this writing, these chips had not been announced as products. A typical commercial offering that meets these conditions is a series of products in the BitWave product line of single and multi-channel RFICs. The claimed product features for the single channel RFIC is shown below: • Single-chip, low-cost, low-power, multiband, multimode transceiver for 4G applications and legacy protocols; • Operates from 700 MHz to 2.7 GHz with bandwidths of 25 kHz to 20 MHz;

• Protocols supported include: LTE, HSPA, WCDMA, EVDO, CDMA2000, GSM, and many others; • 128-pin, 7×7 mm FBGA Package; • 130-nm digital CMOS technology.

Similarly, the multichannel chip has the following claims: • 3 receive channels, 2 transmit channels;

• Simultaneous full-duplex multimode operation;

• Single-chip, low-cost, low-power, multiband, multimode transceiver;

• Operates from 400 MHz to 6.0 GHz with bandwidths of 25 kHz to 20 MHz;

• Implemented using Softransceiver Technology in high-performance, software tunable component blocks. One of the interesting features of Bitwave’s approach is the use of extensive digital programming of the analog operating characteristics such as amplifier bias.

394

Quantitative Analysis of Cognitive Radio and Network Performance

Although there is little engineering experience with exploiting this flexibility, potentially, this could enable another layer of adaptation, as the gains of individual stages are optimized for the existing conditions. A discussion of this design approach is provided by the designers [7, 8]. 22.4 22.4.1

POLICY ENFORCEMENT, DECISION MAKING, AND AIR INTERFACE PROCESSING Air Interface Processing

The processing for wireless platforms has been constrained by three major considerations: throughput, energy, and cost. The most commonly used three technology alternatives are as follows: Application-Specific Integrated Circuit (ASIC): ASICs are the workhorse of the low-cost wireless business. ASICs have a high development cost, but when applied for widely used waveforms and protocols, these costs can be readily amortized. The ASIC is the lowest recurring cost and energy option for signal processing, but it is less than ideal for the cognitive radio application, since it derives much of this capability, affordability, and energy savings by being highly inflexible in its processing. It is certainly possible to imagine a cognitive radio that used ASICs to implement different operating modes and to “cognitively” select from them, but the options within each mode would be constrained to the discrete choices available. While the ASIC is far from optimal for a cognitive radio, its cost and energy performance is the baseline for cognitive radio to achieve a meaningful role in many RF applications. Digital Signal Processor (DSP): DSPs provide a similar design and instruction set architecture to GPP or RISC processors, with an emphasis on fast arithmetic, pipelined operation, and typically, integer modes of operation for high speed. There is a ceiling on their performance, as the maximum processing rate cannot simultaneously perform encoding, waveform generation, and other control functions of many of the waveform and modulation modes that may be desired. They typically consume more power than an ASIC, but less than an FPGA. Field Programmable Gate Array (FPGA): FPGAs are a hybrid product, with aspects of both hardware design and software programming. They could be considered to be the “assembly language” of signal processing. Once highly expensive, some performance points have become cost-competitive to ASICs

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and DSPs in some applications. Typically, FPGA behavior is specified in a hardware description language that, in effect, defines the schematic interconnecting the elements of the FPGA. FPGAs contain many computational and other processing elements that can be structured to construct highly parallel computational engines well suited to wideband signal processing. FPGAs have high throughput, but generally are the highest power consumption of the signal processing alternatives. All of these technologies continue to evolve following the general profile of digital chip density improvements. The rapidly increasing cost of ASIC design and reduced costs for FPGAs has created an increasing interest in FPGAs even for production projects. An emerging technology is the development of massively multicore processors, such as the Tilera line and Coherent Logix HyperX series. These are extensions of the multicore CISC processor trend, except using from 30 to 100 homogeneous DSP or RISC cores together with sophisticated memory and input output interconnections. An emerging candidate for cognitive radio implementation is processors designed for gaming and high performance graphics. Unlike heterogeneous multicore processors, these devices include multiple processing resources, each appropriate for different processing tasks. These devices have the advantage of the very large volume applications for which they are designed, which can make them attractive for the lower volume prototyping of cognitive radios. These processors potentially offer the best of many worlds. The discrete nature of the processor architecture promises the ability to finely control the power consumption through activation and deactivation of individual processors. When all processors are activated, the throughput is significantly more than a single processor, and comparable to high performance FPGAs. This architecture could be enabling of the true vision of software defined and cognitive radio. 22.4.2

Cognitive Radio Decision Processing

Of all of the technology needs of cognitive radio, the most severe shortfall may be in the complex decision-making processing needs, at least when the constrained volume, mass, thermal dissipation, and energy limitations of mobile platforms are considered. The cognitive radio will have to address the two processing categories (endogenous and exogenous reasoning) introduced in Chapter 19. While the device may elect to trade processing complexity for processing resources in its endogenous optimizing processes, the node will always have a responsibility to fully enforce the entire policy regime associated with any spectrum it might consider.

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Quantitative Analysis of Cognitive Radio and Network Performance

Cognitive radio research has been performed using a number of processing models. One partitioning of them is whether their behavior is predetermined (by declarative policy expression such as a predicate calculus or procedural algorithms) or derived through learned experiences (such as a neural network, Bayesian inference, or other training process). These techniques do not have a well established set of computer science techniques associated with them. Of course, a cognitive radio could always be developed using conventional programming, but the complexity of the problem and its interaction across layers of software would imply that procedural implementations might soon become the kind of complex, nonintuitive software that characterizes many of the legacy operating systems and application suites. Cognitive radio could be one of the potential early adaptors of these technologies. Table 22.1 illustrates the applicability of each to the two classes of decision making introduced in Chapter 19. Both of these techniques are undeveloped and in need of applications that can both validate their utility, and provide “use cases” for their developers. Table 22.1 Applicability of Processing Methods Declarative Policies

Machine Learning

Endogenous Reasoning

Highly deterministic and predictable. However, may require extensive programming and validation of algorithms and implementation. Would not improve performance with operating resources.

Avoids extensive programming and validation. Nonoptimal decisions would only impact the mode making them, so they would be local, and presumably not repeated.

Exogenous Reasoning

Highly deterministic and predictable. However, may require extensive programming and validation of algorithms and implementation.

Has the potential risk that the device could either not detect negative effects, or, even if they are detectable, cause interference events during the process of learning. Additionally, learning new environments might result in interference. Would cause regulators to have concerns about nondeterministic behavior.

In the field of policy-based operation, a general framework for network policy control is available from Strassner [9]. Experimental results for such implementations have been reported from the DARPA XG Program [10, 11]. Both IEEE

Desirable Cognitive Radio Implementation Technology Developments

397

and SDRF have initiated development of standards for policy expression [12, 13]. However, these efforts are currently language design focused, and no large-scale implementation work is continuing after the conclusion of the DARPA XG effort. As mentioned in Chapter 4, the viability of machine learning-based approaches for link improvement has received a significant amount of experimental validation [14–17]. Similar to the experimentation with policy languages, they have focused on exploiting existing learning technology. As such, they validate the utility of these techniques in specific algorithm areas, but fundamental tool development is required to validate their applicability to a larger set of problems, and ability to scale in resource consumption and range of conditions with competitive approaches. An exciting hybrid is the integration of machine learning with policy-based assertions as first-order predicate calculus based on machine learning “training” [18]. This formal framework “converts” learned actions into predicate calculus expressions that can then be integrated into a declarative structure such as in a policy-based framework. This technique could bridge the two approaches into an integrated decision process that can leverage the best of both. EXERCISES 22.1

Current filter technology often forces a choice between high levels of input signal attenuation and high filter selectivity. Develop an analysis of how this trade should be analyzed. Reflect the probability of occurrence in the Chicago environment.

22.2

Extend this analysis of the previous problem to reflect additional filter coverage at the cost of increased signal attenuation.

22.3

Describe an experiment that would “train” a cognitive radio to avoid strong signal environments that might yield intermodulation energy in the signaling channel.

22.4

Describe the minimum functions that an experiential learning technology would have to address to be viable to be included in a cognitive radio. Do the same for a declarative policy engine.

398

22.5

References

You are tasked to analyze the necessity to establish “receiver standards” for several bands that are being made available for cognitive radio, as well as some existing bands. You are to consider: (a) TV white space operation in the digital TV broadcast bands; (b) A cognitive radio-only band, with no guarantees of noninterference; (c) The ISM band; (d) The aviation control and navigation band. DSA may be allowed in all of these bands. Note that one premise of this chapter is that the use of CMOS may result in lower quality receivers in the future. For which bands would you recommend receiver standards, and what would be the process to establish values for front-end linearity and filter bandwidth in these bands?

References [1] H. Dayal, “Variable bandwidth, wide tunable frequency, voltage tuned filter,” International Journal of RF and Microwave Computer-Aided Engineering, vol. 14, no. 1, pp. 64–72, 2004. [2] G. M. Rebeiz, RF MEMS: Theory, Design, and Technology, New York: Wiley Interscience, 2003. [3] J. Brank, J. Yao, M. Eberly, A. Malczewski, K. Varian, and C. Goldsmith, RF MEMS-Based Tunable Filters. New York: John Wiley & Sons, 2001. [4] P. F. Marshall, “Progress towards affordable, dense and content focused tactical edge networks,” IEEE Military Communications Conference (MILCOM), Nov. 2008, pp. 1–7. [5] S. M. Hasan and S. W. Ellingson, “Design and development of an evaluation board with RFIC Ver. 4,” Virginia Tech, VA Tech 22, http://www.ece.vt.edu/swe/chamrad/, 2007. [6] S. Hasan and S. W. Ellingson, “Multi-band public safety radio using a multi-band RFIC with an RF multiplexer-based antenna interface,” Proceedings of the SDR 08 Technical Conference and Product Exposition, 2008. [7] E. L. Org, R. J. Cyr, G. Dawe, J. Kilpatrick, and T. Couniha, “Software defined radio—different architectures for different applications,” Proceeding of the SDR 07 Technical Conference and Product Exposition, 2007. [8] K. Gulati, M. Peng, A. Pulincherry, C. Munoz, M. Lugin, A. Bugeja, J. Li, and A. Chandrakasan, “A highly integrated CMOS analog baseband transceiver with 180 MSPS 13-bit pipelined CMOS ADC and dual 12-bit DACs,” IEEE Journal of Solid-State Circuits, vol. 41, no. 8, pp. 1856–1866, Aug. 2006. [9] J. Strassner, Policy-Based Network Management: Solutions for the Next Generation. Kaufmann, 2004.

Morgan

References

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[10] F. Perich, “Policy-based network management for NeXt Generation spectrum access control,” 2nd IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, Apr. 2007. [11] D. Elenius, G. Denker, M. Stehr, R. Senanayake, C. Talcott, and D. Wilkins, “CoRaL—policy language and reasoning techniques for spectrum policies,” IEEE Workshop on Policies for Distributed Systems and Networks, June 2007. [12] M. Cummings, S. Li, B. Fette, M. M. Kokar, S. Li, B. Fette, B. Lyles, P. F. Marshall, and D. Hillman, “Activities of SDR Forum MLM Working Group on a language for advanced communication systems applications,” Software Defined Radio Technical Conference, Washington, D.C., 2008. [13] M. Kokar, D. Hillman, S. Li, B. Fette, P. F. Marshall, M. Cummings, T. Martin, and J. Strassner, “Towards a unified policy language for future communication networks: a process,” 3rd IEEE Symposium on New Frontiers in Dynamic Spectrum Access Networks, Oct. 14–17, 2008, pp. 1–10. [14] A. MacKenzie, J. Reed, P. Athanas, C. Bostian, R. Buehrer, L. DaSilva, S. Ellingson, Y. Hou, M. Hsiao, J.-M. Park, C. Patterson, S. Raman, and C. da Silva, “Cognitive radio and networking research at Virginia Tech,” Proceedings of the IEEE, vol. 97, no. 4, pp. 660–688, Apr. 2009. [15] F. Ge, Q. Chen, Y. Wang, C. Bostian, T. Rondeau, and B. Le, “Cognitive radio: from spectrum sharing to adaptive learning and reconfiguration,” 2008 IEEE Aerospace Conference, March 2008, pp. 1–10. [16] C. Rieser, T. Rondeau, C. Bostian, and T. Gallagher, “Cognitive radio testbed: further details and testing of a distributed genetic algorithm based cognitive engine for programmable radios,” IEEE Military Communications Conference, vol. 3, Oct. 2004, pp. 1437–1443. [17] M. ElNainay, F. Ge, Y. Wang, A. Hilal, Y. Shi, A. MacKenzie, and C. Bostian, “Channel allocation for dynamic spectrum access cognitive networks using localized island genetic algorithm,” 5th International Conference on Testbeds and Research Infrastructures for the Development of Networks and Communities and Workshops, Apr. 2009, pp. 1–3. [18] C. Clancy, J. Hecker, E. Stuntebeck, and T. O’Shea, “Applications of machine learning to cognitive radio networks,” IEEE Wireless Communications, vol. 14, no. 4, pp. 47–52, Aug. 2007.

Chapter 23 Future Research Needs Towards a Cognitive Radio Ecosystem 23.1

INTRODUCTION

Much of the current cognitive radio research is focused on existing problems in wireless technology, and clearly it is essential that cognitive radio technology offer useful solutions to these immediate problems. However, we should also be cognizant that when cognitive radio deployment occurs, a consequence will be new problems and emerging issues. The deployment of tens of cognitive radios will have little impact: the deployment of hundreds of thousands will change the nature of the spectrum environment, require new paradigms in network operation, and require policy and control solutions that are beyond those envisioned today. In particular, the inclusion of dynamic and opportunistic spectrum access within the cognitive radio will fundamentally change the nature of a radio deployment due to significant reduction in the constraints of spectrum availability and management. It is important that research look ahead to this epoch, since there is little benefit in removing one technological obstacle only to immediately encounter another. A particular need that cognitive radio will accelerate is the transition from reliance on wireless networking practice to reliance on wireless network science and theory. Current wireless networks are either simple enough (such as WiFi access points, cellular, and point-to-point links) or sparse enough (military devices) to make current discrete techniques practical. Future cognitive radios offer the potential to create spectrum, network density, and interaction challenges beyond those currently encountered in wireless practice, and more complex than the hierarchical and decoupled autonomous systems in the core of the Internet. 401

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Current network and wireless practice is to isolate effects across systems and layers. With cognitive radios sensing the environment, and reacting to this perception, the fundamental approach of “isolation” will be inapplicable. In fact, these devices will form an “ecosystem” whose members interact directly and richly. One recurring characteristic of changes in ecosystems is that response is often unexpected and emergent. Just as introducing nonnative species can change the balance of an ecosystem through invasive domination, a responsive wireless device will have an impact that will vary depending on the behaviors of the other devices. An important lesson in ecosystem analysis is that in complex environments, it is neither desirable nor possible to analyze the population in terms of its individual members. Instead, populations are considered as a whole, and their aggregate characteristics and dynamics are the target for understanding. Discretely simulating all seven layers of interaction within and across hundreds of thousands of nodes may be both impossible and unnecessary. Instead, the analysis of these cognitive networks will have to transition to more fundamental understanding of the closedform dynamics. For example, what is the rate of change of frequencies as a function of the spectrum occupancy threshold? How does bandwidth needed vary as a function of user correlation?

23.2

DENSITY AND SCALING

Chapter 12 discussed the effect of overhead in DSA coordination scaling. However, the scaling of self-forming and peer-to-peer networks has even more fundamental challenges. The first fundamental change that cognitive radio can (and presumably will) induce is in the density of the network. Prior work in MANET, self-forming networks, and related technologies has generally focused on discovering and exploiting all of the possible connectivity. At some level of network density, this situation will reverse; the network will need to organize its topology to reduce the complexity and interactivity of the connectivity. Figure 17.4 illustrated the conventional view of the deployment of a MANET. In this model, the network’s strategy is to maximize connectivity by rendezvousing all of the nodes onto a single frequency. Unfortunately, this architecture has been shown to have severe scaling limitations due to (among other reasons) mutual interference between the nodes. Even nodes that cannot demodulate a signal may be interfered with by its presence. The interference range is well in excess of the communications range. A simple measure of this network is to note than only one node can transmit at any one time, despite the

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number of transceivers that are present. This is a strong intuitive argument against its scalability. This architecture is enabled by DSA and cognitive radio technologies. The management of this many frequencies in a complex and constantly changing network would be impractical for any static spectrum assignment strategy, and is probably only practical with automated management and interference control. The second enabling consideration is the use of the adaptation of a cognitive radio to use lower cost and performance transceiver components in each of the nodes, making the multitransceiver configuration affordable at a lower cost than if a single, high-quality transceiver was utilized. Not only does the cognitive radio functionality improve the link-level performance, it can be the key factor in replicating the physical layer. This provides a mechanism to address the scaling limits imposed by node to node interference that are otherwise inherent in the MANET network structure. The issues of how to route and determine appropriate topologies with this many degrees of freedom are fundamental research questions that arise with the integration of DSA and advanced networking.

23.3

COGNITIVE ALGORITHMS AND REASONING EXPRESSIONS

It is certainly a positive indicator for the field of cognitive radio research that there is a large research community attacking a large number of problems and opportunities in the field. The challenging problem is that as we develop and validate individual techniques to meet cognitive radio technology challenges such as channel selection, modulation selection, power control, filter settings, and other options, we have no common or community accepted framework to integrate these technologies. The likely result of this will be islands of technology capabilities, each targeting specific problems but not readily integrated with other solutions to other problems. Even if we can develop the range of policies and controls shown in Figure 19.2, how will we integrate their operation onto a single device? Many researchers have reported on the objective of using policy languages to control network devices [1, 2] and the DARPA XG program has reported development of two policy reasoners for spectrum policy conformance reasoning [3, 4]. This is a reasonable start, but a broader base of the community must begin to be involved in contributing the insight from their research areas to these ontologies, semantics, and required syntaxes, and a more extensive set of reasoning domains needs to be considered in their formulation. While the mechanism for interdomain collaboration has often been through published work, our objective should be a

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structure sufficiently abstract and domain-independent that the devices can integrate the behavioral expressions resulting from contributions across the field, and create a logical collaboration among them at run time. There has been progress reported in this area. SCC41 has established a policy language as one of its goals [5]. The SDR Forum has also reported progress on development of a joint industry-academic community project [6]. This effort states it objectives as: “. . . the following two deliverables: 1. An integrated set of ontologies for the particular subdomains. 2. A formal language, common to all the subdomains, capable of expressing domain specific policies.” Additionally, the reasoning engine is only one half of the tool set that must integrate into a cognitive radio. There is an equal need for learning-based engines that offer so much promise for the endogenous portion of the decision process. Lastly, there is the challenge of integrating these two very different structures into a cohesive theory and implementation of these devices. 23.4

ASSURING COGNITIVE RADIO STABILITY

The logistics of acquiring, programming, and operating wireless nodes has generally limited the size and extent of the networks that have been used to prototype cognitive and ad hoc wireless technology. When the node count is in the hundreds we can certainly treat each node as an individual, and the dynamics of the system are sufficiently limited so that it can be analyzed by discrete tools. As we contemplate networks that have large membership, not in the hundreds, but in the hundreds of thousands of nodes, then explicit understanding of the behavior of individual nodes becomes unachievable, and a transition to a more systemic treatment of network behavior is mandatory. We need to move from network practice to network science to provide the tools necessary to understand the dynamics of these interactions. Large-scale simulation tools have given us the ability to achieve insight into the operation of individual nodes within routing networks. What will make cognitive radio even more challenging is that the complex networks envisioned not only interact through the network mechanisms, they also interact through the spectrum they share at the physical layer, have the necessity and capability to continually morph the connectivity and content locations, and can even adapt to, and potentially change, the user behavior. Conventional radios assume (and generally receive) spectrum that is intended to make node operation orthogonal. A cognitive radio

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will share spectrum, so decisions that are made on one node will ripple through neighboring nodes, and through them, potentially to all the nodes within a network. A cognitive radio network will consist of at least physical, media access, and network layer processes that are each making decisions that ripple across nodes and between layers. These stimuli transit the network at different rates, are damped, or initiate cascading changes at different points, and have different responses at each layer. The problem becomes a system exhibiting characteristics of transient response, coupling, damping, and oscillation. An example of such behavior could be as simple as just one radio starting to transmit on a frequency, forcing ten radios to move to new frequencies, each of which in turn force ten more to do the same. At the same time as this ripple of frequency changes moves through the network, the network layer would be attempting to update routing, even as the network connectivity was changing. Some of these effects are shown in Figure 23.1, providing change stimuli, coupling, and damping effects on a potentially oscillatory system. Stability analysis of layer 3 has become an accepted technique, but a system that has this scale of interaction with and through the environment, users, devices, and applications of a cognitive radio network is inherently much more complex.

Stimuli Frequency Changes Incumbents Motion Entering Nodes Exiting Nodes Demand

Damping Sub-Net Boundaries Bandwidth Margin Link Margin

Coupling Routing Topology Changes Interference Events Queuing

Figure 23.1 Illustrative cognitive network stimuli, coupling, and damping effects.

Neel provides an argument that the behavior of spectrum interaction can become stable even given locally optimizing behaviors, but this analysis must be

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extended to include the interaction of these processes with the other layers and the effects between them [7]. The change in focus from node to system behavior is imperative to adoption of cognitive radio technology. One appeal of cognitive radio is that it can manage dense environments; the concern is that it may become so complex we cannot prove it will have an acceptable range of behavior and performance! The critical consideration of both network and spectrum density is inherent in any of the concepts of spectrum sharing. One effect of current spectrum management has been to protect most radios from detrimental effects of their environments, including adjacent and out-of-band signals. Where these protections have failed, the effects have been significant, as demonstrated in the case of interference between adjacent cellular services and public safety radio systems in the United States [8]. Since one assumption of cognitive radio is that it will share spectrum, it is reasonable to assume that it will both be required to locate in the proximity of strong signals such as television broadcasts, and it will increase the density of band usage where it is permitted. A quantitative sense of one of these effects is obtained by considering that if a radio had just enough IIP3 performance to not have its noise floor impacted in a given environment, and DSA enables ten times the emitter density (a typical claim for increased spectral usage arising from DSA technology), then energy provided to the front-end would be expected to increase proportionately by 10, and the third order intermodulation products to correspondingly increase by 20 to 30 dB. Density creates its own issues at both the network and the physical layers of the device! Like an ecosystem population mode, it may not be possible to make assertions about the future state of individual organisms, but can predict the aggregate dynamics and end-state of the population as a whole. The need for showing stability of an extremely large number of nodes would argue for a transition from a discrete simulation to a formal analytic model of the dynamics of a system of nodes. This transition might have significant implications for how the design of such large-scale and highly coupled systems is approached. Proof of a formal stability may have to become a fundamental driver in MAC, network, and applications layers. Proof that they have appropriate damping of impulsive modes, resist excitation, randomize responses, vary resonant modes, and the kind of design decisions normally associated with a control process, may need to become integral in the design of these layers and of equal or greater importance than today’s concerns with throughput and latency. The understanding of these complex interactions needs to move from programming “if, then, else” logic to differential equations, modes, and generalized expressions of stability and

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equilibrium conditions that can be proven for all cases, rather than shown for a single or set of discrete cases. Additional insight may flow from increases in understanding of “network science” and “social networks” [9]. These create general concepts of network behavior and performance that includes the application and user behavior. In particular, they identify and describe the correlation of user behavior. Earlier, the concept of the cognitive wireless network as an ecosystem was introduced. The traditional approach to understanding network performance has been to model all of the node’s discrete behavior. As the number of nodes increases, the ability to analyze and understand each individual is increasingly impractical. Treating the aggregation of nodes as an ecosystem provides a conceptual basis for stepping back from discrete understanding, and examining issues of dynamics and stability from the perspective of the population as a whole, rather than from discrete understanding of each node individually.

23.5

DECISION THEORY IN COGNITIVE RADIO

There is an emerging body of literature that details the optimizations that can be performed on channels, and we are seeing the beginning of a similar literature in the area of more complex adaptations. In the implementation of DSA functionality, the sensed information required to make spectrum decisions can be obtained at relatively low cost in energy and channel time. This class of sensing can generally be considered accomplished as unilateral operations performed by the nodes without explicit coordination. On the other hand, channel measures such as delay spread or MIMO Eigen-matrix can only be obtained when the channel is established and able to exchange information in sufficient quantity to satisfy the decision needs of the adaptation algorithms. The actual amount of channel state information may not be extensive, but the time to perform rendezvous, synchronization, as well as measurements may be significant, particularly if the time is long compared to the decorrelation time of the channel information that is being acquired. Although it has been shown that the use of this information can improve the performance of devices, the progression of the field requires we now consider the benefits we obtain from additional channel information compared to the cost of obtaining it. Designers generally have at least statistical knowledge of the environment in which their devices will operate, so the problem is one of decision theory: “How much is a decision improved if uncertainty is reduced, compared to the cost of obtaining the additional information?”

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For example, a radio might have both a constant envelope and high peak average ratio (PAR) waveform available, and would select based on the measured delay spread and the range of intersymbol interference (ISI) that would occur. For example, in deciding to use this waveform, the question that the cognitive radio technology must address is: How much is a better decision between these waveforms worth, compared with the cost of initially and periodically measuring the channel characteristics? One can imagine that some decisions would be better left with some uncertainly, but there has been little formal research that provides a framework to investigate these trades.

23.6

INFORMATION THEORY IN COGNITIVE RADIO

One inspiration for cognitive radio adaptations can be obtained from inspiration derived from the difference between the information theoretic bounds on the capability of a network and what is achieved in real operation. The difference between the bounding capability and the level achieved is an indication of the potential targets for the application of the technology. This perspective can be applied to several of the domains of interest to cognitive radio, including: Spectrum Utilization: Shannon [10] provides a mechanism to measure the effect of noise on the capacity of a channel and its maximum capacity. This has led to analysis of the effectiveness of modulation techniques in terms of their ability to approach the Shannon bound on efficiency. This can lead to techniques to understand the optimality of modulations and link operation. An extension of this theory bound to networks would provide an effective mechanism to assess the effectiveness of technology solutions, not only on the aggregate throughput of the network, but in terms of each individual region of the network. Information Content: The focus of efficiency in most networking is transmitted bits. A more meaningful measure would be the information entropy of the information products.1 This formulation of information throughput is insightful. This measure would recognize that the true measure of a network is its ability to deliver information, not bits, and would integrate the performance of content positioning within the network with other considerations of network operation. More transmitted bits may not equal better network utility. An information 1

Information entropy is the true amount of information in a set of random variables such as messages. The entropy HP (in binary bits) of X, containing a set of possible values (x1 , ..., xn ) with probabilities p(xi ) is − n i=1 p(xi ) logb p(xi ). High probability messages have less information value than highly random messages [11].

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theory-based measure would recognize that the best way to increase network performance may be in avoiding the transmission of data, rather than in increasing the rate of transmission. 23.7

SECURITY

There are several clear lessons from the growth of the Internet. One is that security cannot be applied to a system after it is designed and deployed, and the other is that no matter how obscure a security weakness is, someone will discover a way to both exploit it, and to profit from the exploitation. Therefore cognitive radio will be best served if these issues are addressed as an element of the fundamental research investigations rather than as an appliqu´e after the problems become apparent in operation. This is not to say that this field has been ignored by researchers; as discussed in Section 4.3.3, work on establishing trust and avoiding malicious attack [12, 13] has been reported, and Clancy has reported a significant amount of research in inherent security for cognitive networks [14]. The cognitive radio community will have to develop this subspecialty to the same level of attention that the fixed Internet community now addresses it. 23.8

CONCLUSIONS

The last 20 chapters have attempted to describe a vision of cognitive radio that is highly integrated with the operation of the devices at one level, and with the fundamental information theory concepts at the other. Its fundamental argument is that cognitive radio should be considered not just as an newly emerging field of research, but as one that can offer fundamental solutions to problems that are otherwise intractable when approached from within the traditional disciplines. Most of the conventional radio communications literature and even a large portion of the cognitive radio literature approach links from the perspective of stand-alone performance in an undisturbed environment. Few wireless services operate in this mode. It is the rich interaction of environments, hardware limitations, and user behavior that may best justify cognitive radio’s role in the wireless discipline. To claim that place, it is essential that the performance impacts of cognitive radio not be notional behaviors, but quantified in terms to support the investment in research and deployment required to fulfill its promise.

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Cognitive radio offers a unique nexus for interdisciplinary research. The fundamental solutions will be derived from integrating contributions from a wide range of disciplines. Whereas waveform design is a study of details within electrical engineering, cognitive radio will require reaching into other disciplines to understand their contributions to developing quantified models of complex environments, describing methods and policies, demonstrating scalable and stable modalities, understanding the human behavior implications for network operation, and balancing resource investments on a millisecond-by-millisecond basis. This book has demonstrated that generalized descriptions of some of the environments can be developed and applied to derive general modes of cognitive radio performance. These generalized expressions of opportunity can be enablers for integrating the contributions from these disciplines into cognitive radio technology developments that can fundamentally reshape the wireless discipline. EXERCISES 23.1

Chapters 5 through 22 have identified a number of techniques and analysis approaches. Identify five radio or wireless services. Identify which approaches could have a significant impact on their performance.

23.2

Using the results from the previous problem, describe how you would quantify the potential contribution of cognitive radio techniques to two of these applications.

23.3

The focus of this book has been on cognitive radio applications to communications systems. Sensing systems (primarily radar) are also significant users of spectrum. Describe which of the techniques in this book could be applied to radars, and what the likely impact might be.

23.4

Based on the equation in the footnote for determining the information content of messages, compute the information theoretical number of bits on a portion of a complex web home page compared to the transmitted textual material.

23.5

Using the spectrum samples, determine the relative benefit of various spectrum sampling intervals from the perspective of a radio that is using spectrum sampling solely to avoid interference to its own channel.

References

23.6

411

Consider a simple ecosystem of wireless nodes. Each node has a particular probability of interfering with any of the other nodes if it relocates to the same frequency. Nodes take a certain time to relocate after being interfered with. What is the rate of change of frequencies on this network? (Note: This problem requires experience with derivatives in calculus.)

References [1] J. Strassner, Policy-Based Network Management: Solutions for the Next Generation. Morgan Kaufmann, 2004. [2] M. Kokar, D. Hillman, S. Li, B. Fette, P. F. Marshall, M. Cummings, T. Martin, and J. Strassner, “Towards a unified policy language for future communication networks: a process,” 3rd IEEE Symposium on New Frontiers in Dynamic Spectrum Access Networks, Oct. 14–17, 2008, pp. 1–10. [3] D. Elenius, G. Denker, M. Stehr, R. Senanayake, C. Talcott, and D. Wilkins, “CoRaL—policy language and reasoning techniques for spectrum policies,” IEEE Workshop on Policies for Distributed Systems and Networks, June 2007. [4] F. Perich, “Policy-based network management for NeXt Generation spectrum access control,” 2nd IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, Apr. 2007. [5] IEEE Standards Coordinating Committee 41, “Dynamic spectrum access networks,” http://www.scc41.org/, 2007. [6] M. Cummings, S. Li, B. Fette, M. M. Kokar, S. Li, B. Fette, B. Lyles, P. F. Marshall, and D. Hillman, “Activities of SDR Forum MLM Working Group on a language for advanced communication systems applications,” Software Defined Radio Technical Conference, Washington, D.C., 2008. [7] J. Neel, “Analysis and design of cognitive radio networks and distributed resource management algorithms,” Ph.D Dissertation, Virginia Polytechnic Institute, Blacksburg, VA, 2006. [8] L. Luna, “NEXTEL interference debate rages on,” Mobile Radio Technology, Aug. 1, 2003. [9] L. Freeman, The Development of Social Network Analysis. Vancouver: Empirical Press, 2006. [10] C. E. Shannon, “A mathematical theory of communication,” Bell System Technical Journal, pp. 79–423 and 623–656, July and Oct. 1948. [11] T. M. Cover and J. A. Thomas, Elements of Information Theory. New York: John Wiley & Sons, 1991. [12] T. X. Brown and A. Sethi, “Potential cognitive radio denial-of-service vulnerabilities and countermeasures,” Proceedings of the International Symposium on Advanced Radio Technologies, Boulder, CO, Feb. 2007, pp. 44–51. [13] R. Chen, J. Park, Y. Hou, and J. Reed, “Toward secure distributed spectrum sensing in cognitive radio networks,” IEEE Communications Magazine, Apr. 2008, pp. 50–55. [14] T. Clancy and N. Goergen, “Security in cognitive radio networks: threats and mitigation,” 3rd International Conference on Cognitive Radio Oriented Wireless Networks and Communications, May 2008, pp. 1–8.

Appendix A Internet Protocol Networking for Cognitive Radios A.1

INTRODUCTION TO IP NETWORKING

This appendix is intended to provide a brief introduction to the Internet Protocol (IP) as it applies to cognitive radio. It is not intended to provide a comprehensive treatment of either IP or of cognitive networking, but is instead focused on those aspects that most impact the decisions that must be made by a cognitive radio and its adaptation to the characteristics of this protocol. While IP was not initially developed to optimize the performance of wireless networks, it has been so successful in the fixed infrastructure, its extension to wireless applications was both inevitable and driven by the installed base, the depth of experience, and the lack of any competing, wireless-focused alternative. Although this appendix is titled “Internet Protocol Networking for Cognitive Radios,” it is broader in scope than just the network Layer 3 protocol. “IP” has become the title for an entire ecosystem of protocols and standards that is the dominant force and technology in communications. It is the basis for the pervasive Internet of today, including industry and standardized protocols. Standards exist at all layers of the Internet, from Ethernet to the application standards that are the basis of the service architecture of the web. This appendix will examine the IP ecosystem from the perspective of wireless in the second decade of the twenty-first century. None of this should be taken as a criticism of the design of the Internet. What started as a network to connect, at best, several dozen hosts, has grown to billions of users. Its design has withstood the test of time, and has scaled by over eight orders of magnitude. And the Internet deployed 413

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today was not even the Internet in its final form. Vint Cerf has noted that the actual configuration was designed for a single test, and not intended as the final design.1 There are two versions of IP in current use. At the time of this writing, the bulk of usage is IP version 4 (IPv4), which is the baseline for the Internet. Recently, IP version 6 (IPv6) has started to be deployed in limited use. Although there are many differences between them, the primary change is the expansion of addresses from 32 bits in IPv4 to 64 bits under IPv6. The following discussion is generally applicable to both versions.

A.2

BASIC IP NETWORKING PRINCIPLES

A.2.1

IP Numbering

An IP address consists of two segments. The upper segment is the network address, and the lower one is the host within the network. In principle, the network could be a campus and the host ID a specific computer on the campus. The Internet routing structure might deliver traffic to the campus router and the campus router would resolve which node corresponds to the host address IP and then deliver the packets by whatever network is used locally. To improve network performance, network administrators can define a subnetwork structure, which partitions the host addresses within a single IP network. This provides the flexibility to segment the network so that the external world only is aware of routes to the one external address, but when the packet arrives at the destination router, it can be directed to the specific subnet without having all of the traffic appear on all of the enterprise LAN. Once traffic has arrived at the destination network (defined by the upper segment of the address), the router must decide how to map the host ID address to the addressing scheme of the local host network. For discussion purposes, we will assume it is an Ethernet LAN. For Ethernet, this is performed by the Address Resolution Protocol (ARP). If the router does not know the mapping from IP address to Layer 2 MAC address, the router transmits a request for the node with the specified IP address to reply, which provides its own Layer 2 address. The router then inserts the IP packet as the LAN payload and delivers it via the LAN address. A very detailed discussion of the requirements for IPv4 routers is provided in the applicable Request for Comment (RFC) [2]. IPv6 has similar intent, although it is 1

For example, see a talk by Vint Cerf at Singularity University [1].

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more elegant and flexible due to the expanded address space available for network addresses. In the original Internet, the structure of the IP number was organized by the class of addresses. Each class partitioned the address at a different point, creating either more network or more node addresses. There were a small number of class A addresses defined, each of which had a large number of node addresses. Class C addresses had a longer network address, and so were more numerous, but had a shorter node address, so each subnet was smaller. To accommodate a more flexible partitioning of IP addresses, Classless Inter-Domain Routing (CIDR) was provided by RFC 4632 [3] and made the fixed structure of address definition or partitioning unnecessary. In addition to the IP numbering system, the Internet has an additional number system for autonomous system (AS) identification. The Internet-wide routing is between ASs. An AS is a set of Internet networks that have multiple points of attachment to the Internet and common routing policies. If different routing policies are needed, then separate ASs are required. If an IP network has only one point of contact with the Internet, then it must be within an AS with all other networks reliant on that point of contact. Because of the requirement for dual homing and unique routing requirements, ASs are highly aggregated. The consequences of this level of aggregation will be discussed later in this appendix. A.2.2

IP Routing

Internet Protocol routing is partitioned into interior and exterior routing. Interior routing is within an AS domain; exterior routing is the process of routing packets between them. The primary interior protocol is Open Shortest Path First (OSPF), whose IPv4 implementation is defined by RFC 2398 [4]. OSPF is a link state routing algorithm that defines the lowest cost path between networks within the AS. Through the OSPF protocol, routers determine adjacencies to other directly connected routers. Sets of routers are designated “areas” and may be connected to the backbone, or at the other end of the possible connectivity, they may be “stubs,” which have no direct connect to any external network. This OSPF structure typically provides IP routing for traffic with an enterprise, such as the intranet services, access to enterprise email servers, local file shares, and other internal services. OSPF does not scale to arbitrary networks, so some enterprise systems use Border Gateway Protocol (BGP) to connect the individual interior OSPF domains. Another interior protocol is the Intermediate System to Intermediate System (IS-IS) protocol, typically used by Internet service providers.

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BGP is the primary protocol between the ASs that comprise the Internet. These are the intermediate systems (IS). BGP is defined by RFC 427 [5], and is the basis for the decentralized routing within the Internet. BGP establishes peer relationships with the BGP routers with which it has direct connectivity. BGP routers obtain connectivity information about IS and AS networks that are reachable from each, and using a number of algorithms, determine the best path to take to reach each IS for which it is presented traffic. A BGP router has to “know” the Internet. An OSPF router only has to be aware of its own network, but BGP may be presented traffic to any destination within the Internet. Maintaining that awareness is an increasingly challenging problem. Although each router is generally quite stable, the aggregate effect of route changes to all of the routers on the Internet creates a considerable rate of change and associated update burden. Some IS networks have multiple connections to the Internet, a configuration referred to as “multihoming.” Multihoming creates shorter paths to an IS throughout the Internet, but at the cost of replicating the network address through several routes. The BGP router not only has to route to the node; it must select the best route to the node. Routing information was once growing exponentially. While that growth has been reduced to linear through route consolidation and other efforts, the trend will not reverse. In summary, the heart of the Internet (BGP) is not equipped to handle highly dynamic topologies.

A.3

IMPACTS ON WIRELESS DEVICES AND NETWORKS

IP is well established and will likely be the basis for networking technology for at least the next few decades. The deployment of IPv6 is a significant expense, and it is not likely that this investment will be abandoned anytime soon. It is therefore important to examine the operation of wireless, mobile, and self-forming networks in the context of the overall IP ecosystem. This discussion will differentiate between wireless networks and wireless devices that provide access to the fixed infrastructure. Equipment such as WiFi served laptops or smartphones are wireless devices, but they are not wireless networks. The network they utilize is fixed. The wireless connection is strictly an extension of the same framework as a local area network, except a wireless link is used instead of an Ethernet. They are typically one hop away from the Internet and associated infrastructure. They typically only serve a single address and mostly a single device. These devices introduce new challenges to the IP networking, but

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in large part, they have similar characteristics to many other IP devices. A cell phone might “roam” within a provider (or partner) network but it is still within the provider’s IP address space and is not significantly different than a laptop that moves around a large campus wired IP network. At any one time, it is connected to a single tower; it has a single IP address assigned through the cellular service.2 Its IP assignment might change since it is not static and it must be a member of the network it is associated with at any one instant. It cannot run symmetric services such as a Web host since its IP address is dynamic. This is not wireless networking in the context of this book. Let us instead imagine a public service use case such as introduced in Chapter 2. A public safety network is established with self-forming network technology (MANET) operating over cognitive radios. This network is capable of connecting to any IP connectivity to support emergency operations. It has a mix of high-speed local and collaborative services that cannot be supported through backhaul to a single control or server node. Much as FM “push to talk” police radios were not dependent on central infrastructure, this network should be capable of supporting any communications process that is consistent with the ability of the link layer to form the required connections. This network node’s traffic might be distributed using existing sources of connectivity when emergency responders were addressing typical conditions, but would aggregate when emergency response was required. Many of the experiments with MANET networks have focused on the routing within the MANET, or provided a single access path (and network address) from the MANET to the exterior network. When these arbitrary restrictions are removed, the networking problem is fundamentally changed. Consider our public safety example on the scale of a U.S. county with an operating area of hundreds of km2 . In this case, one would not want all routing to occur solely as a wireless LAN (the AS), as that would require routing traffic to a gateway at the edge of the network (many km away) even when Internet access (through connectivity via other AS domains) to the interior of the network was plentiful. The desirable capability would have units access the Internet through any available IP connectivity, but it surely should not route all traffic to a single point of presence, as depicted in much of the MANET literature. The issues that this philosophy raises for the IP infrastructure routing are described in the next sections.

2

Some phones may also have an IP address through some other network connection such as Wi-Fi, but this IP connection operates independently from the IP connection of the service provider, and applications on the device must decide which to use.

418

A.3.1

Quantitative Analysis of Cognitive Radio and Network Performance

Wireless Network Topology

The public safety emergency response network constitutes a single administrative domain. In the Internet structure, that would make it an independent system (IS). However, at some times, members of that IS may not be in connectivity to each other. In this case, the IS model is less effective because the intra-IS communications must take place over resources and routes that are within the BGP scope, since there are no adjacent IS paths. However the network association of these disconnected nodes is the same IP network! A police car might drive by Wi-Fi hot spots, use a cellular provider, access other radios within the network for connectivity, and maybe even access a fixed point of presence in a very short time. Yet, this structure of mixed internal and external routing is completely incompatible with the taxonomy of the exterior routing process. This scenario breaks down when constructed using the AS/IS/IP network taxonomy. Forcing traffic to flow along paths compliant to that organization or taxonomy could result in highly illogical, performance-constraining, or failed routes. With true wireless mobility, the boundary between the internal and external routing is not absolute. Further, IP routing is typically updated based on 3 minutes of link loss, which then begins a process of route convergence. Consider how many different IP networks might be encountered within one 3-minute interval, much less the entire routing convergence period. A.3.2

Wireless Device Identity

When using a cell phone, the telephone infrastructure establishes identity independent of the IP topology. When using e-mail, a central infrastructure service holds the e-mail for nodes until they request it. This architecture works because the e-mail service is a fixed service. Fixed addressing is appropriate to nodes whose relationship to the topology rarely changes, and whose addresses therefore are stable. This assumption is much less viable for wireless devices that will migrate between access locations. The problem arises because the IP address is used as both a topological value (through the network/subnetwork structure) and an identity value; the IP address is used to link interactions into sessions and to ensure that secure “conversations” (such as provided by IP security (IPSEC)) services are not vulnerable to insertion from intermediate or masquerading nodes. In the fixed Internet, devices were considered to be tethered to a fixed location, and therefore their connection to the Internet was stable. It was hard to imagine a room-sized computer roaming

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419

throughout the address space of the Internet. It was therefore reasonable to associate node identity (the IP number) with the path to reach the node. These assumptions are less than reasonable for a wireless device, which may navigate between a home Wi-Fi network, 3G cellular services, a corporate wireless network, and perhaps the Wi-Fi network at a cafe. In the IP framework, the IP number would be a function of the node and path through which each of the connections was established. However, that would mean that a Voice over Internet Protocol (VoIP) conversation would have to change IP numbers, web sessions would be fractured, and TCP transfers and IPSEC sessions would fail, as their operation is dependent on an unchanging IP network address. A change in IP number would appear as a man-in-the-middle attack. Transactions in process, such as TCP transfers, would fail as the IP address changed. Contrast this behavior with a cell phone number which can roam the world without change because it is associated with the identity of the phone (or its owner) and not the path by which it is reached at any moment in time. Another elegant approach is by Skype, which uses a directory service to cross-reference an identity value (username) to a current IP address. Once the cross reference has been performed, the actual exchange is performed peer-to-peer, so no infrastructure resources are needed if the nodes can connect directly. This approach is pleasing from a wireless perspective because it enables the least bandwidth resources to be used, and it maintains the ability of the wireless devices to operate independently once they have performed the cross-reference, and so long as the IP number does not change.3 IPv6 introduces a similar feature by providing “care of” addresses. These provide IP addresses that remain static as the device roams other networks. The device reports its current network location, and the “care of” agent routes the traffic directly to the dynamic address. This would appear to have solved many of the issues raised earlier, but the solution is not complete. Imagine two responders want to exchange video when they are 100 feet apart, yet 20 miles from the physical location of their agent. That video would have to go 20 miles to and 20 miles back from the agent. Instead of leveraging the close proximity of the responders to provide additional capability, all data would have to traverse the entire network. If the backhaul was unavailable, two individuals who could see each other physically would be unable to initiate a communications session. If, on the other hand, they do resolve the address to the current physical connection’s IP network, then they will fail whenever the backhaul address changes, as discussed previously. Neither approach can create robust communications networks. 3

If it does change during a session, it is subject to the same issues as described above.

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Quantitative Analysis of Cognitive Radio and Network Performance

A.3.3

Opportunistic and Multihoming Internet Access

The network cannot have any preconceived communications paths. So it must be flexible and opportunistic in how it acquires backhaul and internal bandwidth. No single connection may have the reliability and capacity to support the network, so it would want to make all of the available paths be usable, yet without partitioning the network based on the network addresses of the resources it was sharing. We have seen that BGP has scaling issues when large enterprises multihome their Internet access. Imagine the scaling issues if every wireless device could independently multihome itself. A.3.4

Naming Services

Most use of the Internet relies on the Domain Name Server System (.com, .edu, .org, .net, .uk). These domain name servers are reasonably stable, but they are not local. In our emergency response network, how would it operate if it was disconnected from the core Internet and had to locate resources within itself, but without any name server? This is related to the concern stated earlier regarding the need for decentralized mechanisms to establish, and maintain, naming structures. Name services on the Internet were designed from the start to operate hierarchically, from the root domain down to the specific domain. Such a structure does not scale to small, and potentially isolated nodes.4 A.3.5

Dynamics

Lastly, all of these changes happen very fast. The Internet anticipated that the topology would change slowly and that link status might change more rapidly. With opportunistic wireless systems, changes in both topology and link status will occur extremely rapidly, particularly in comparison to the detection interval (180 seconds) and convergence time of the fixed Internet. A.4

ASSUMED COST OF BANDWIDTH AND NETWORK SCALING

One of the fundamental principles of the Internet is that bandwidth is scalable. If you want to double bandwidth, you just pay for, at most, two connections, rather 4

With an apology to the technical work of the author’s own institute, who developed the original Internet Domain Name Server System, the Information Sciences Institute (ISI) at University of Southern California’s Viterbi School of Engineering.

References

421

than one. In Chapter 2, it was pointed out that Shannon [6] teaches this is not true for spectrum and energy limited systems. In these systems, each incremental bit requires doubling of the energy. Therefore, capacity in wireless networks is constrained to a relatively limited range of spectral bandwidths. Bandwidth is not scalable, and spectrum is finite, so the effectiveness with which bandwidth is used becomes increasingly important as the ratio of information rate to channel bandwidth is increased. The nonscalability of wireless bandwidth is a fundamental difference from wired Internet practice. A.5

SUMMARY

This chapter is not intended to analyze the design of the Internet, but to point out that the full realization of the promise of cognitive radio and wireless networks will require technology changes at more than the physical layer. It is inevitable, and even desirable, that wireless will have to coexist with the fixed Internet. However, its design principles are quite distinct and both wireless and fixed Internet communities will have to be involved in mechanisms that can create the adaptation and dynamics that fully support wireless networking. There has been a tendency to port Internet technology directly to wireless without challenge. However, the Internet user increasingly uses wireless as the preferred modality. When the fixed Internet community is unresponsive to the need for adapting Internet standards to better exploit wireless capabilities, the author suggests you show them a picture of customers in line, overnight, waiting for the next generation wireless smartphone. Then, ask if they can find a picture of a line of excited customers waiting for the next generation cable modem. References [1] Singularity University, “Vint Cerf—The Internet Today,” http://www.youtube.com, Oct. 3, 2009. [2] The Internet Society, “Requirements for IP Version 4 Routers, RFC 1812,” June 1995. [3] The Internet Society, “Classless Inter-Domain Routing (CIDR): The Internet Address Assignment and Aggregation Plan, RFC 4632,” Aug. 2006. [4] The Internet Society, “OSPF Version 2, RFC 2398,” Apr. 1998. [5] The Internet Society, “A Border Gateway Protocol 4 (BGP-4), RFC 4271,” Jan. 2006.

422

References

[6] C. E. Shannon, “A mathematical theory of communication,” Bell System Technical Journal, pp. 79– 423 and 623–656, July and Oct. 1948.

Appendix B DVD Contents The accompanying DVD provides a number of resources that parallel the material in the book, as well as the data that was used to generate much of the analysis material in the book.

B.1

ORGANIZATION OF FILES

The top-level organization of the DVD is shown in Figure B.1. The first folder (Book Figures) provides full color .pdf files for the graphics in the book. The second folder (Link Spread Sheet) contains the link margin spreadsheet used in Chapter 2. The third folder (Data Bases) contains processed frequency domain samples, as well as summary files that include the statistical characteristics of each of the sample sets. The last folder (MATLAB Routines) provides computation of Monotonic Index distribution parameters, and some example code to access the databases through a control file to automate database collection selection.

B.2

BOOK FIGURES

The book figures are in folder Book Figures. The content of this folder is shown in Figure B.2. Each book chapter has its own folder, and within each folder the figures are titled Figure1, Figure2, and so forth. 423

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Quantitative Analysis of Cognitive Radio and Network Performance

Figure B.1 DVD root level.

Figure B.2 Book chapter figures.

DVD Contents

425

Table B.1 DVD File Set Index

B.3

Index

Collection

1 2 3 4 5 6 7 8

Chicago New York Day 1 New York Day 1 Vienna Tysons Riverbend NRAO Aggregated

COMMUNICATIONS LINK MARGIN SPREADSHEET

The Link Spread Sheet folder contains the link analysis spreadsheet from Chapter 2 in Microsoft Excel format. The link margin spreadsheet implements the computations provided in Chapter 2 for the example link characteristics, using the same variable definitions as in Table 2.3 and Table 2.4. An image of the worksheet page is shown in Figure B.3. The boxed values are the ones to be entered to compute a link margin; the other values are computed by the spreadsheet.

B.4 B.4.1

SPECTRUM MEASUREMENTS DATA Overview

Spectrum samples are provided for the six spectrum measurement campaigns described in Chapter 6. For ease of processing, a set of analysis results for each set is also provided, which enables the closed-form analysis to be performed without processing the sample sets individually. The folder containing these samples is shown in Figure B.4. The raw data is provided to support other analysis methodologies. Each of the sample sets includes three files: one with raw spectrum data, one with constant signaling bandwidth, spectrum occupancy analysis, and one with proportional bandwidth, front-end analysis. In the following discussion, xxx represents one of the spectrum collection names (from Table B.1).

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Quantitative Analysis of Cognitive Radio and Network Performance

Simple Link Budget Analysis Tool Chapter 2, Tables 2.1 - 2.2 Example Transmitter Link Margin Transmit Power Xmit Antenna Gain(Loss) Path Loss Fade Margin Power at Rcvr Rcv. Antenna Gain Rcvr Power Required Energy at Receiver Thermal Noise Additional Rcvr Noise Estimated Rcvr Noise Floor Required SNR Minimum Rcvr Power

10.0 -1.0 -74.0 -20.0 -85.0 -1.0 -86.0

dBm dB dB dB dBm dB dBm

-114.0 15.0 -99.0 10.6 -88.4

dBm dB dB dBm

Link Margin

2.4 dB

Min Transmit Power

7.6 dBm

Computations - Transmitter Side Tranmit Power Frequency Range Path Loss

0.01 2.4 50 74.0

watts is GHz is meters dB

Computations - Receiver Side Noise Temperature 290 K Rcvr Bandwidth 1,000,000 Hz Thermal Noise Power -114.0 dBm Modulation Required Eb/No Data Rate Required Energy/Noise

10.6 dB 1,000,000 bits/sec 10.6 dB

Figure B.3 Link margin spreadsheet.

10.0 dBm 0.125 meters

DVD Contents

Figure B.4

B.4.2

427

Spectrum sample databases folder.

Frequency Domain Files

The xxxSampleSet.mat file contains a set of spectrum power measurements at 25 kHz intervals for time index t and frequency index f. The specific frequencies for each index are provided by the variables in the structure. The power values are in dBm, and there is one column per measurement interval. The file is a MATLAB structure, including the fields shown in Table B.2. The Measurements(t,f) matrix is a set of power measurements in dBm. Each entry is the integrated power over BandStep of spectrum, which is the frequency bin size, at time t. The first entry frequency is From MHz, and they are linearly incremented. Each time increment is approximately one minute. This structure can be loaded into MATLAB (assuming you have set a path to the database folder and its subfolders) by: load (’ChicagoSampleSet.mat’), or whatever collection name (from Table B.1) is desired, substituting for Chicago. A screenshot of this structure is shown in Figure B.5.

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Quantitative Analysis of Cognitive Radio and Network Performance

Table B.2 SampleSet Frequency Domain Structure Filelds Field

Contents

name BandStep From Measurements(t, f)

Created CreatedDateString

The collection name (in this case Chicago) The bandwidth of each frequency bin, in MHz (in this case 25 kHz) The lower frequency of the first bin, in MHz (in this case 30 MHz) A matrix of measurements. In this case, there are 40 time intervals and 114,783 frequency bins. Mean power over the entire duration of the sample set for the given frequency bin Date file was created, in MATLAB format date Date file was created, as a string

Index

Meaning and Range

t f

Time Index, from [1.. size(Measurements,1)] Frequency Index, from [1.. size(Measurements,2)]

IntegratedPower(f)

Figure B.5 MATLAB structure for frequency domain.

DVD Contents

B.4.3

429

Spectrum Occupancy Statistics

The xxxSpectrumOccupancyStatistics.mat file contains a set of spectrum occupancy statistics (where xxx is the name of a collection from Table B.1). This loads the MATLAB structure SpectrumOccupancyStatistics, with the fields shown in Table B.3. Note that some fields that are in the structure are not required in the analysis. There are two data analysis results in this data set. One provides the CDF of the spectrum occupancy distribution, and one provides the distribution parameters, and the polynomial coefficients for the estimation function. The CDF is computed for a range of values starting at MindBm, and incrementing by Step to a maximum of MaxdBm. The amplitude value of each CDF index a is in AmplitudeList (1, a). Population provides the total number of measurements equal to or less than this value in AmplitudeList (1, a). CDFPopulation is the normalized (0..1) probability distribution for this same data. Both CDFPopulation and Population are indexed (f, a), where f is the filter index, and a is the amplitude index. MindBm, MaxdBm, a, b, Mean and Variance contain the beta distribution characteristics associated with each bandwidth value. Polya, Polyb, PolyMinE, and PolyMaxE provide the polynomial estimators for the distribution functions. FWidth (1, f) is the bandwidth in MHz for the respective entry. AmplitudeList (1, a) is the amplitude for an entry. All of the collections provide 40 different bandwidth analysis sets. A screenshot of this structure is shown in Figure B.6. B.4.4

Front-End Statistics

The xxxFEEnergyStatistics.mat file contains a set of front-end energy statistics (where xxx is the name of a collection from Table B.1). This loads the MATLAB structure SpectrumOccupancyStatistics, with the fields shown in Table B.4. Note that some fields that are in the structure are not used in the analysis. There are two data analysis results in this data set. The CDF is computed for a range of values starting at MindBm, and incrementing by BandStep to a maximum of MaxdBm. The value of each amplitude index a range is in AmplitudeList (1,a). Population(f, a)provides the total number of measurements equal to, or less than the value of Amplitude(1, a) for filter index f. CDFPopulation is the normalized (0..1) probability distribution for

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Table B.3 SpectrumOccupancyStatistics Structure Filelds Field

Contents

The collection name Date file was created, in MATLAB format date The median value of the beta distribution mean parameter for each bandwidth AmplitudeList(1, a) The power for each increment MindBm Minimum power for the CDF array MaxdBm Maximum power for the CDF array Population (f, a) The cumulative number of measurements under the threshold power, per filter bandwidth CDFPopulation (f, a) Normalized Population, providing the CDF of the distribution between MindBm and MaxdBm FWidth (1, f) The width of each bandwidth entry nChannelPoints The number of bandwidth entries a (1, f) The value of the beta distribution α parameter for each bandwidth b (1, f) The value of the beta distribution β parameter for each bandwidth ChannelMindBm (1, f) The minimum value of the energy for each bandwidth maxV (1, f) The maximum energy parameter for each bandwidth PolyMinE (o) The coefficients of the polynomial equations for the F Emin estimator PolyMaxE (o) The coefficients of the polynomial equations for the F Emax estimator Polya (o) The coefficients of the polynomial equations for the α estimator Polyb (o) The coefficients of the polynomial equations for the β estimator Mean (1, f) The mean value of the distribution for each bandwidth Variance (1, f) The value of the variance of the distribution for each bandwidth ChannelMindBm (1, f) The minimum energy parameter for each bandwidth MindEnergydBmBin Lowest energy in a bin for the entire sample set MinEnergydBmHz Lowest energy in power per hertz name Created Median (1, f)

Index

Meaning and Range

a f o

CDF amplitude index, from [1.. (MaxdBm-MindBm)/Step] Signaling bandwidth index, from [1.. nChannelPoints] Polynomial coefficient order, specific to the polynomial

DVD Contents

Figure B.6 MATLAB SpectrumOccupancyStatistics structure.

431

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Quantitative Analysis of Cognitive Radio and Network Performance

this same data. Both CDFPopulation and Population are indexed (f, a), where f is the filter index, and a is the amplitude index. MindBm, MaxdBm, a, b, Mean and Variance contain the beta distribution characteristics associated with each bandwidth entry. Polya, Polyb, PolyMinE, and PolyMaxE provide the polynomial estimators for the distribution functions. Bandwidths(1, f) is the bandwidth ratio for the respective entry. Amplitude(1, a) is the amplitude for an entry. In all of the collections, there are eight different bandwidth analysis sets provided. A screenshot of this structure is shown in Figure B.7.

Figure B.7 MATLAB FEEnergyStatistics structure.

DVD Contents

433

Table B.4 FEEnergyStatistics Structure Filelds Field

Contents

name The collection name Created Date file was created, in MATLAB format date CreatedString Date file was created, in string format Bandwidths (1,f) List of bandwidth factors for each increment b MindBm Minimum power for the CDF array MaxdBm Maximum power for the CDF array Step Increment in power for the CDF array AmplitudeList (1, a) The power for each increment Population (f, a) The cumulative number of measurements under the threshold power, per filter bandwidth CDFPopulation (f, a) Normalized Population, providing the CDF of the distribution between MindBm and MaxdBm BandStep The step in bandwidth for each preselector filter increment palpha (o) beta distribution α parameter in any filter of that bandwidth pbeta (o) beta distribution β parameter in any filter of that bandwidth MinEnergy (1, f) Minimum power in any filter of that bandwidth MaxEnergy (1, f) Maximum power in any filter of that bandwidth PolyMinE (1,f) The coefficients of the polynomial equations for the F Emin estimator PolyMaxE (1, b) The coefficients of the polynomial equations for the F Emax estimator Polya (1, f) The coefficients of the polynomial equations for the α estimator Polyb (1, f) The coefficients of the polynomial equations for the β estimator Mean (1, f) The value of the CDF distribution mean parameter Variance (1, b) The value of the CDF distribution variance parameter Index

Meaning and Range

a f o

CDF amplitude index, from [1.. size(Bandwidths, 2)] Preselector bandwidth index, from [1.. nChannelPoints] Polynomial coefficient order, specific to the polynomial

434

B.5 B.5.1

Quantitative Analysis of Cognitive Radio and Network Performance

MATLAB ROUTINES Monotonic Indices

The DVD includes a structure containing the polynomials to determine the values of the beta distribution parameters in two structure files, SOMonotonic and FEMonotonic. Example code for applying these structures to compute the synthetic spectrum distribution of Chapter 8 is shown in Figure B.8. load (’FEMonotonic’) load (’SOMonotonic’) IDensity = .0864 IIntensity = 130 BW = .20; b0 =25;

% Set the following index variables

DV = [1; IDensity; IDensityˆ2; (IIntensity/10); ... (IIntensity/10)ˆ2; (log10(b0)); (log10(b0))ˆ2] SOa =10ˆsum (SOMonotonic.aIndexPoly .* DV) SOb = exp( sum(SOMonotonic.bIndexPoly .* DV)) SOmin = sum(SOMonotonic.MinIndexPoly .* DV) SOmax = sum(SOMonotonic.MaxIndexPoly .* DV) DV = [1; IDensity; IDensityˆ2; (IIntensity/10); ... (IIntensity/10)ˆ2; (log10(BW)); (log10(BW))ˆ2] FEa =10ˆsum (FEMonotonic.aIndexPoly .* DV) FEb = 10ˆ sum(FEMonotonic.bIndexPoly .* DV) FEmin = sum(FEMonotonic.MinIndexPoly .* DV) FEmax = sum(FEMonotonic.MaxIndexPoly .* DV)

Figure B.8 Monotonic Index Distribution Determination

B.5.2

MATLAB Access Routine

Each file type can be manually loaded into MATLAB, but to make the process easier, a small routine is available in the MATLAB routines folder to manage the files. This routine can load any of the sample collections sets and any one of the analyzed aggregation files. The program should set the path to the Samples.mat file. Similarly, the sample set index (set) should be set to the value of the collection

DVD Contents

435

Table B.5 DVD File Type Index Index

File Type

Contents

1 2 3 4

SampleSets FEEnergyStatistics

Frequency domain measurements Front-end energy distributions Unused Signal bandwidth statistics

SpectrumOccupancyStatistics

files, as shown in Table B.1. Your program can then set the index of the file type and collection to open any of the files, or loop through all of the files. The FileType index is shown in Table B.5. set = from Table B.1; FileType = from Table B.5; CFn = ’Samples.mat’; load (CFn);

List of Acronyms and Abbreviations ACK ADC AGC AI AODV API ARP AS ASIC AWGN BAA BAW BER BGP BLAST BPSK CDF CDMA CER CFP CISC CMNI CMOS CR CSMA CTS DARPA DBPSK DFS

Acknowledgment Analog to Digital Converter Automatic Gain Control Artificial Intelligence Ad-hoc On Demand Distance Vector (Routing) Application Program Interface Address Resolution Protocol Autonomous Systems Application Specific Integrated Circuit Additive White Gaussian Noise Broad Area Announcement Bulk Acoustic Wave Bit Error Rate Border Gateway Protocol Bell Labs Adaptive Space Time (Coding) Binary Phase Shift Keying Cumulative Density Function Code Division Multiple Access Cost Estimating Relationship Connection Formation Probability Complex Instruction Set Computer Consider Marginal Noise Impacts Complementary Metallic-Oxide Silicon Cognitive Radio Carrier (Collision) Sense Multiple Access Clear to Send Defense Advanced Research Projects Agency (United States) Differential Binary Phase Shift Keying Dynamic Frequency Selection 437

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DFT DNS DOS DSA DSP DSR DSSS DST DTN DTN DQPSK DWDM DYSPAN Eb/No ECC EDAC EHP EIRP ELB EM EPLRS EVM FCC FDD FDMA FE FFT FM FPGA FRS FSK GA GaAs GPP GSM HDTV HF HTML

Discrete Fourier Transform Domain Name System Denial of Service Dynamic Spectrum Access Digital Signal Processor Dynamic Source Routing Direct Sequence Spread Spectrum Dempster-Shafer Theory Delay Tolerant Networking Disruption Tolerant Networking Differential Quadrature Phase Shift Keying Dense Wave Division Multiplexing Dynamic Spectrum Access Networks (Conference) Energy per Bit over Noise Error Correcting Code Error Detection and Correction Equivalent Hardware Performance Equivalent Isotropic Radiated Power Emergency Locator Beacon Electromagnetic Enhanced Position Location Reporting System Error Vector Magnitude Federal Communications Commission (United States) Frequency Division Duplex Frequency Division Multiple Access Front-end Fast Fourier Transform Frequency Modulation Field Programmable Gate Array Family Radio Service Frequency Shift Keying Genetic Algorithms Gallium Arsenide General Purpose Processor Global System for Mobile Communications (Groupe Spcial Mobile) High-Definition Television High Frequency Hyper-Text Markup Language

List of Acronyms and Abbreviations

IETF IF IFFT IIP2 IIP3 IMD IP IP IPSEC IRG IS ISI ISI ISIS ISM ISO IT ITU LAN LB LBT LMR LNA LO LTE LTE-A MAC MAN MANET MEMS MIMO MMIC MSK NET NIB NTIA NRAO NSF

Internet Engineering Task Force Intermediate Frequency Inverse Fast Fourier Transform Input Intercept Point, Second Order Input Intercept Point, Third Order Intermodulation Distortion Internet Protocol Intellectual Property Internet Protocol Security Internet Research Group Intermediate System Intersymbol Interference Information Sciences Institute Intermediate System to Intermediate System Industrial, Scientific, and Medical International Organization for Standardization (in French) Information Technology International Telecommunication Union Local Area Network Locator Beacon Listen Before Talk Land Mobile Radio Low Noise Amplifier Local Oscillator Long Term Evolution Long Term Evolution-Advanced Media Access Control Metropolitan Area Network Mobile Ad Hoc Network Microelectromechanical Systems Multiple Input/Multiple Output Monolithic Microwave Integrated Circuits Minimum Shift Keying Network Layer Noninterfering Basis (or Not to Interfere Basis) National Telecommunications and Information Administration National Radio Astronomy Observatory National Science Foundation (United States)

439

440

OFCOM OFDM OFSK OIP3 OLSR OSI OSPF OWL PAR PAUR PCR PD PDF PER PFA PHY PLNFF PNSS PQBF PSAT PSK P2P Q QAM QPSK QoS RF RFC RFIC RISC RKRL ROC RRM RSSI RTS SAW SCC SD

Quantitative Analysis of Cognitive Radio and Network Performance

Office of Communications (United Kingdom) Orthogonal Frequency Division Multiplexing Orthogonal Frequency Shift Keying Output Intercept Point, Third Order Optimized Link State Routing Open System Interconnection Open Shortest Path First Ontological Web Language Peak to Average Ratio Peak to Average Usage Ratio Policy Conformance Reasoner Probability of Detection Probability Density Function Packet Error Rate Probability of False Alarm Physical Layer Pick Lowest Noise Floor First Pseudonoise Spread Spectrum Pick Quietest Band First Power, Saturated Phase Shift Keying Peer-to-Peer Quality Factor (generally used to describe bandwidth of filters) Quadrature Amplitude Modulation Quadrature Phase Shift Keying Quality of Service Radio Frequency Request for Comment Radio Frequency Integrated Circuit Reduced Instruction Set Computer Radio Knowledge Representation Language Receiver Operating Curve Radio Resource Management Received Signal Strength Indication Request to Send Surface Acoustic Wave Standards Coordinating Committee Spectrum Usage Density

List of Acronyms and Abbreviations

SDR SFDR SNR SiGe SINR SOP SPD SPTF SRI SSC SSI SSID SSR TCP TDD TDMA TV UDP UHF URL US UWB VHF VoIP VPN WAN Wi-Fi WiMax WISP WLAN WNaN WRAN WRC XG XML YIG

Software Defined Radio Spur-free Dynamic Range Signal-to-Noise Ratio Silicon Germanium Signal to Interference and Noise Ratio Spectrum Outage Probability Spectral Power Density Spectrum Policy Task Force SRI International Shared Spectrum Corporation Single Source Interference Service Set Identifier (802.11) System Strategy Reasoner Transmission Control Protocol Time Division Duplex Time Division Multiple Access Television User Datagram Protocol Ultrahigh Frequency Uniform Resource Location United States of America Ultrawideband Very High Frequency Voice over Internet Protocol Virtual Private Network Wide Area Network Wireless Fidelity (IEEE 802.11a, b, g, n) Worldwide Interoperability for Microwave Access Wireless Internet Service Provider Wireless Local Area Network Wireless Network after Next Program (DARPA Program) Wireless Regional Area Network World Radio Congress Next Generation Communications Program (DARPA Program) Extensible Markup Language Yttrium Iron Garnet

441

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Quantitative Analysis of Cognitive Radio and Network Performance

List of Symbols AManual

Manual Deconfliction Area

Area that is deconflicted for individual radios operating in manual spectrum assignment.

Aspectrum

Spectrum Availability

Probability of spectrum availability.

at

Amplitude Threshold

Threshold signal density for declaring a channel as occupied. Typically dBm/hertz for the resolution bandwidth of the sensor.

B

Bandwidth

Bandwidth for Shannon channel capacity.

b0

Occupied Bandwidth (in MHz)

Signal bandwidth which will be the basis for signal-to-noise determination.

bandusagei

User Bandwidth

The instantaneous spectrum usage by user i.

bandwidth0

Available Spectrum Bandwidth

The total bandwidth made available to a set of users.

BenefitFENoise

Benefit of Cognitive Radio Noise Reduction

The improvements in overload intermodulation through cognitive adaptations.

BenefitOverload

Benefit of Cognitive Radio Overload Reduction

The improvements in overload probability through cognitive adaptations.

BW

Preselector Bandwidth Ratio

Preselector bandwidth that will be provided to the first (or later) receiver amplifier stages, expressed as a proportion of the center frequency of the filter.

C

Channel Capacity

Capacity for Shannon-bound channel.

dC-C

Cognitive Radio Separation

Distance between the two cognitive radios.

dNC-CRR

Cognitive and Noncognitive Radio Separation

Distance between the cognitive radio receiver and the noncooperative transmitter.

dT-T

Transmitter Separation

Distance between the two transmitting radios.

duty

Network Duty Cycle

Ratio of time transmitting on a given network to the total time (aggregated across nodes).

EffPA

PA Efficiency

Efficiency of power amplifier.

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Quantitative Analysis of Cognitive Radio and Network Performance

fc

Filter Center Frequency

The middle of the filter range fc = flow + (fhigh flow )/2.

FEmax

Maximum Front-End Preselector Power

The maximum power within the specified BW parameter between flow and fhigh .

FEmin

Minimum Front-End Preselector Power

The minimum power within the specified BW parameter between flow and fhigh .

FEpowerin

Front-End Input Power

The total power provided to the LNA and later stages.

FEα

Front-End Energy Distribution alpha parameter

The beta distribution alpha parameter for a given preselector bandwidth parameter.

FEβ

Front-End Energy Distribution beta parameter

The beta distribution beta parameter for a given preselector bandwidth parameter.

fhigh

Frequency Highest

The highest frequency of the highest preselector setting.

flow

Frequency Lowest

The lowest frequency of the lowest preselector setting.

Fmargin

Fade Margin

Degree of additional fade margin added to the link budget in the worst-case range condition. 1 = no fading, 10−2 = 20-dB margin.

GLNA

LNA Gain

The gain of the LNA stage.

I

Individual Node Throughput

The mean bandwidth available (can be sourced) from a node to a random node within a network. Bandwidth used for relayed or routed traffic is not included in this value.

IDensity

Spectrum Density Index

Arbitrary index of the density of signals within a specified spectrum.

IDSA

DSA Performance Index

The ratio of rendezvous time to sensing time.

IIntensity

Spectrum Intensity Index

Arbitrary index of the intensity of signals within a specified spectrum.

IIP2

Second-Order Intercept

The effective second-order intercept of all stages in front of the modem.

IIP3

Third-Order Intercept

The effective third-order intercept of all stages in front of the modem.

kFEmaxi

Front-End Maximum Power Coefficient

The ith order bandwidth polynomial coefficient for the maximum energy of the front-End energy beta distribution.

kFEmini

Front-End Minimum Power Coefficient

The ith order bandwidth polynomial coefficient for the minimum energy of the front-End energy beta distribution.

List of Symbols

445

kFEαi

Front-End α Coefficient

The ith order bandwidth polynomial coefficient for the α term of the front-End energy beta distribution.

kFEβi

Front-End β Coefficient

The ith order bandwidth polynomial coefficient for the β term of the front-End energy beta distribution.

Kpath

Path Loss Constant

Path loss during first unit distance propagation.

kSOmaxi

Channel Maximum Power Coefficient

The ith order bandwidth polynomial coefficient for the maximum energy of the channel occupancy beta distribution.

kSOmini

Channel Minimum Power Coefficient

The ith order bandwidth polynomial coefficient for the minimum energy of the channel occupancy beta distribution.

kSOαi

Channel α Coefficient

The ith order bandwidth polynomial coefficient for the α term of the channel occupancy beta distribution.

kSOβi

Channel β Coefficient

The ith order bandwidth polynomial coefficient for the β term of the channel occupancy beta distribution.

Lmargin

Link Margin

Ratio of energy available at the receiver in excess of that required to demodulate to an acceptable BER to account for all link disturbances. Includes fade margin.

n

Number of Nodes

The quantity of nodes distributed within a given region or as members of a network.

N0

Noise Floor

Total receiver generated and environmental noise.

Needed

Frequency Assignments Needed

Number of frequency assignments that are needed by a pool of nodes.

NPool

Pool Size

Number of radios that are contended for access to the spectrum.

NRecipients

Number of Units

Number of units that must receive a broadcast.

Pbo

IMD3 Noise Within b0 Bandwidth

The total mean power distributed across a signaling bandwidth b0 .

PD

Probability of Detection

The probability of a single sensor scan detecting energy in a channel.

PFA

Probability of False Alarm

The probability of a single sensor scan falsely detecting energy in a channel.

446

Quantitative Analysis of Cognitive Radio and Network Performance

PFEOverloadCR

Noncognitive Radio Probability of Front-End Overload

Probability that a noncognitive radio front-End will be overloaded in a given environment and with stated IIP3 and BW parameters.

PFEOverloadNCR

Cognitive Radio Probability of Front-End Overload

Probability that a cognitive radio front-End will be overloaded in a given environment and with stated IIP3 and BW parameters.

PIMD3

IMD3 Noise Power

Total IIP3 generated noise, expressed as the equivalent signal input power.

Pinterference

Interference Threshold

Maximum energy present on a channel in order for that channel to be reused.

Pinterruption

Probability of Interruption

Probability that the link will fail for environmentally induced causes.

PNC

Power of a Noncooperative Transmitter

Effective isotropic transmit power of a noncooperative node.

PPA

Power Amplifier Out

Power amplifier output power.

Prcvanalog

Analog Receiver Section Power Digital Section Receiver Power

Power for the analog portion of a receiver.

Precieve

Required Receive Energy

Required energy at the receiver in the worst-case range condition.

Psignal

Signal Power

Signal or in-band power.

PSS

Preselector Settings

The number of statistically independent preselector settings that are available within tuning range containing candidate frequencies of operation.

PT

Transmitted Power

Equivalent transmit power from antenna.

Pxmit

Transmit Power Consumption

Transmitter section power consumption, not including PA.

Rwc

Worst-Case Range

Required range corresponding to the maximum fade and the worst case.

S

Signal Energy

Signal energy for Shannon capacity determination.

SDSA

DSA Provided Node Separation Distance

Separation required for nodes operating under DSA principles.

SIE

Spectrum Information Effectiveness

The overall effectiveness of spectrum usage measured proportional to bits/hertz/area.

SINR

Signal-to-Interference and Noise Ratio

Ratio of signal energy to the sum of interference plus noise.

Prcvdig

Power consumption of the digital elements in receiver (A to D and beyond).

List of Symbols

1

447

Smanual

Manually Planned Node Separation Distance

Separation required for nodes operating under manually deconflicted spectrum principles.

SOmax

Maximum Channel Occupancy Energy

The maximum power within any bandwidth in a range of frequencies and location.

SOmin

Minimum Channel Occupancy Energy

The minimum power within any bandwidth in a range of frequencies and location.

SOP

Spectrum Outage Probability

The probability that spectrum energy density will exceed a fixed threshold.

SRCR

Signal at Receiving Cognitive Radio

The signal energy perceived at a receiving cognitive radio.

STCR

Signal at Transmitting Cognitive Radio

The signal energy perceived at a transmitting cognitive radio.

treform

Network Reformation Time

Time, after transmissions terminate, for the dynamic spectrum access network to become available for application traffic.

trelease

Abandonment Time

Time, following tsense , for all dynamic spectrum access devices (within a single network) to abandon the channel, measured when last transmissions terminate.

trendezvous

Rendezvous Time

The mean time for a DSA system to abandon a frequency, and move the network to a new frequency.

tsensing

Sensing Time

Time interval between the start of dynamic spectrum access device/network sensing periods, or the time to complete a cycle of all monitored frequencies.

vin

Input Voltage

Signal voltage presented to the input of the LNA.

vout

Output Voltage

Amplified signal voltage.

xmit

Transmit Duty Cycle

Duty cycle of the transmitter section of a radio.

α

Beta Distribution Parameter

The α parameter of the beta distribution.

α

Propagation Exponent

Reflecting the path loss between transmitter and receiver. Typically between 2 and 3.8.

αbc

Best-Case Propagation1

Propagation coefficient corresponding to the best-case conditions.

The units of distance for propagation are arbitrary, but should be selected such that they are similar to the distance at which the best- and worst-case propagation diverge (i.e., the point at which propagation transitions from direct to diffracted). Typically, this might be 200 to 500m for a low antenna and 1 km for a handheld, and it would be irrelevant for a space link, since all propagation would be r2 at reasonable “look angles” or elevation from the surface.

448

Quantitative Analysis of Cognitive Radio and Network Performance

αcom

Communications Link Propagation Exponent

The propagation exponent between link partners (i.e., the transmitter and the intended receiver(s)).

αint

Interference Propagation Exponent

The propagation exponent between a link transmitter and the unintended receiver(s), for which this emission constitutes interference.

αmean

Mean Propagation Exponent

The mean value of the propagation exponent within a given set of links.

αmin

Minimum Propagation Exponent

The minimum of the propagation exponent within a given set of links.

αtc

Typical Propagation

Propagation coefficient corresponding to the typical conditions.

αvariance

Propagation Exponent Variance

The variance of the propagation exponent within a given set of links.

αwc

Worst-Case Propagation

Propagation coefficient corresponding to the worst-case conditions.

β

Beta Distribution Parameter

The β parameter of the beta distribution.

P(. . . )

Probability Distribution

A probability distribution of the parametric variable.

About the Author Preston Marshall is the director of the Wireless Technology Division at the University of Southern California’s Viterbi School of Engineering Information Science Institute, where he leads research programs in wireless, networking, cognitive radio, alternative computing, and related technology research. Dr. Marshall has 30 years of experience in networking, communications, and related hardware and software research and development. For most of the last decade, he has been at the center of cognitive radio research, including seven years as a program manager for the U.S. Defense Advanced Research Projects Agency (DARPA), where he led many of the key cognitive radio programs, including the neXt Generation Communications (XG) program, Disruption Tolerant Networking (DTN), Connectionless Networking, and the Wireless Network after Next (WNaN) program. These programs demonstrated the viability of key aspects of cognitive radio technology, including dynamic spectrum access (DSA), adaptive wireless networking, content-based networks, and low-cost, DSA-based, multitransceiver adaptive networking. He has numerous published works, and has many appearances as invited or keynote speaker at major technical conferences related to wireless communications. He was awarded the Software Defined Radio Forum’s 2007 Annual Achievement award, the Defense Superior Service Award in 2008, has been a guest editor for IEEE Proceedings, and chairs the Steering Committee for the IEEE International Symposium on Dynamic Spectrum Access Networks (DYSPAN) Conference. Dr. Marshall holds a Ph.D. in electrical engineering from Trinity College, Dublin, and a B.S. in electrical engineering, and an M.S. in information sciences from Lehigh University, Pennsylvania.

449

450

Quantitative Analysis of Cognitive Radio and Network Performance

Index Ad Hoc Networking, 86 Address Resolution Protocol (ARP), 414 Air Interface, 359, 393, 394, see also Modulation, Waveform Algorithms, 2, 7, 30, 77, 88, 89, 92, 93, 181, 182, 200, 202, 220, 250, 270, 275, 387, 396, 407 Alpha-Stable Distribution, see Distribution, AlphaStable Analog to Digital Converter (ADC), 16, 175, 368 Antenna, 17–19, 31, 81, 83, 95, 102, 103, 108, 144, 216, 217, 220, 224, 265, 267, 270, 276, 290, 291 Application Program Interface (API), 297 Application Specific Integrated Circuit (ASIC), 346, 348, 394, 395 Artificial Intelligence (AI), 331 ASIC Ahead, 381 Atmospheric Noise, 389 Attenuation, 19, 32, 34 Automata, 332 Automatic Gain Control (AGC), 30, 127, 170, 345 Autonomous Systems (AS), 401, 415 Availability, 12, 80, 170, 234, 245–247, 354, 361, 362, 364–366, 368 Axiomatic Systems, 338, 339 Bandwidth, 10, 11, 19, 21–23, 25, 26, 28, 30, 32–35, 37, 46, 47, 63, 78, 79, 81,

451

82, 85, 87, 90, 93, 96, 104–120, 123–140, 144–147, 159–172, 175– 178, 180, 181, 187–191, 198, 242, 248, 249, 265–276, 286, 289, 295, 300, 301, 313, 315, 317, 345, 348, 364, 365, 367, 370, 393, 420, 421 Bayesian Inference, 342, 396 Behavior, 2, 3, 58, 63, 64, 67, 250, 328, 331, 402, 404, 406, 409 Belief, 340–342 Beta Distribution, see Distribution, Beta Beta Function, see Function, Beta Binary Phase Shift Keying (BPSK), 26, 27, 84–87 Binomial Distribution, see Distribution, Binomial Bit Error Rate (BER), 1, 30, 33, 127, 170 Bitwave, 393–394 Boltzmann’s Constant, 32, 33 Border Gateway Protocol, see Protocol, Border Gateway Broadcast, 104, 156, 169, 176, 201, 241 Bundles, 315, 384 Caching, 315–320 Carrier (Collision) Sense Multiple Access (CSMA), 223, 266, 346 Cellular Services, 4, 8, 10, 11, 28, 43, 46, 47, 51, 62, 173, 216, 241, 250, 300, 304, 324, 382, 388, 389, 401, 406, 416–419 Cerf, Vint, 68, 414 Classless Inter-Domain Routing (CIDR), 415 Clear to Send (CTS), 215, 219, 223 Code Division Multiple Access (CDMA), 25 Cognitive Logistics, 337

452

Quantitative Analysis of Cognitive Radio and Network Performance

Coherent Logix HyperX, 395 Complementary Metallic-Oxide Silicon (CMOS), 22, 28, 38, 68, 379, 381, 387, 390– 395 Complex Instruction Set Computer (CISC), 395 Consider Marginal Noise Impacts (CMNI), 181 Content-Based Networking, 68, 299, 313–318, 320, 321, 324, 374, 382–384 Cosite Interference Effects, 10, 28, 169, 172, 183, 193 Cumulative Distribution Function (CDF), 110, 117, 119, 130–132, 138–140, 147, 254 Cyclostationary Detection, 61 Decibel, 15, 16, 31 Decision Theory, 89, 90, 155, 340, 343, 344, 407, 408 Declarative Language, 58, 64, 65, 328, 330 Defense Advanced Research Projects Agency (DARPA), 60, 63, 65, 67, 68, 92, 196, 235, 302, 304, 317, 329, 340, 346, 374, 378, 380, 381, 384, 393, 397 Delay Tolerant Networking (DTN), 63, 68, 245, 315, 317, 374, 382–384 Delay Tolerant Networking Research Group (DTNRG), 316, 382 Demodulator, 21 Dempster-Shafer Theory (DST), 343, 344 Denial of Service (DOS), 66 Device-to-Device (D-D) Communications, 62, 324 Differential Quadrature Phase Shift Keying (DQPSK), 23 Digital Logic, 391 Digital Signal Processor (DSP), 346, 348, 394, 395 Digital Television, see High Definition Television (HDTV) Dipole, 18 Direct Conversion Receiver, 22, see also ZeroIF Receiver

Direct Sequence Spread Spectrum (DSSS), 25, 216, 265 Disaster Communications, see Emergency Communications, Hurricane Katrina, and Tsunami Disruption Tolerant Networking (DTN), 63, 68, 316, 317, 374, 382–384 Distribution Alpha-Stable, 251 Beta, 110–120, 133, 135–137, 144, 171, 185, 186 Binomial, 179, 246, 363 Gamma, 254, 256 Gaussian, 108, 110, 199, 300 Normal, 246 Poisson, 116, 250 Domain Name System (DNS), 10, 420 Duty Cycle, 227, 229, 234, 236, 245–249, 261, 334, 336, 356, 366, 368, 369 Dynamic Frequency Selection (DFS), 250 Dynamic Range, 16, 22, 30, 77, 94, 95, 123, 126, 127, 346, 391 Dynamic Spectrum Access (DSA), 2–5, 11– 13, 27–30, 42, 46–53, 60–69, 77, 80, 85, 87, 93–98, 101, 104, 160, 192, 193, 195–212, 215, 217–222, 225–227, 229, 232–236, 241–262, 265, 266, 279–292, 295, 299–308, 314, 323, 329–334, 354, 365, 366, 374–381, 387, 401–403, 406, 407 Dynamic Spectrum Access Networks Conference (DySPAN), 59, 68, 374 Ecosystem, 402, 406, 407 Electromagnetic Field, 18 Emergency Communications, 5, 9–11, 97 Emergency Locator Beacon (ELB), 234 Emission Mask, 82 Encryption, 315 Endogenous Reasoner, 82, 87, 88, 92, 329, 330, 332–337, 395, 396, 404 Energy Efficiency, 1 Energy per Bit over Noise (Eb /No ), 31, 33, 34, 127, 266, 267, 353, 355 Enhanced Position Location Reporting System (EPLRS), 376

Index

Entropy (Information), 408, 409 Equalization, 36 Error Detection and Correction (EDAC), 21, 22, 24, 25, 335 Ethernet, 216, 225, 296, 298, 413, 414, 416 EXCEL (Microsoft), 34 Existential Quantification, 338–340 Exogenous Reasoner, 82, 83, 87, 88, 92, 220, 329–337, 395, 396 Experimentation Cognitive Radio, 11, 13, 59, 63, 64, 66–68, 374, 378–382 DTN, 63, 64, 68, 382–384 Dynamic Spectrum, 59, 67, 329, 373–382 Learning, 64 Policy Reasoning, 65 TV White Space, 61, 62, 67 Extensible Markup Language (XML), 81, 296, 298 Fade Margin, 8, 31, 32, 34, 155, 229, 231, 233, 235, 236, 242–245 False Alarm, 151–156 Family Radio Service (FRS), 8, 319 Fast Fourier Transform (FFT), 345, 348 Federal Communications Commission (FCC), 4, 47, 50–52, 62, 67, 69, 82, 228 Femptocells, 62 Field Programmable Gate Array (FPGA), 346– 348, 394, 395 Filter, 19–21, 81, 89, 123–130, 132, 133, 160, 163–165, 167–170, 172, 173, 176, 178, 181, 182, 184, 185, 190–192, 348, 365, 369, 370, 374, 381, 382, 387–390, 403 Bulk Acoustic Wave (BAW), 389 Ceramic, 389 Digital, 1 Discrete (LC), 1 Group Delay, 389 Microelectromechanical Systems, 390 Ripple, 389 Skirt, 388 Super-Conducting, 390 Surface Acoustic Wave (SAW), 1, 389 Varactor, 389, see also Varactor

453

Yttrium Iron Garnet (YIG), 390 Firewall (Network), 335 FM Broadcast, 104 Frequency Division Duplex (FDD), 25, 26, 49, 51, 173, 265 Frequency Modulation (FM), 83, 417 Fresnel Zone, 279 Front-End (Receiver), 12, 27, 28, 30, 31, 38, 80, 83, 94–96, 104, 123, 124, 128, 135–137, 139, 140, 159–161, 167– 170, 172, 175–178, 181–184, 187, 191–193, 353–355, 362, 364, 367– 370, 374, 380, 381, 388, 406 Function Beta, 119–121, 246 Gamma, 256 Incomplete Beta, 119–121, 246 Lambert W, 274 Gallium Arsenide (GaAs), 391 Gamma Distribution, see Distribution, Gamma Gamma Function, see Function, Gamma Garage Door Openers, 50 Gaussian Distribution, see Distribution, Gaussian General Purpose Processor (GPP), 346, 348, 394 Genetic Algorithms, 63, 64 Group Delay Spread, 389 Haitian Earthquake (2010), 5 Hamming Distance, 24 Handle References, 314 Haykin, Simon, 3, 4 Hidden Node(s), 49, 281 High Definition Television (HDTV), 392 High Frequency (HF), 388, 389 Hurricane Katrina, 5, 319 Hyper-Text Markup Language (HTML), 296 IEEE 802.22, 5, 52, 59, 61, 69 IEEE Standards Coordinating Committee (SCC), 50, 59, 64, 69 IEEE Std 1900.1-2008, 13 Image Response, 20, 21, 392

454

Quantitative Analysis of Cognitive Radio and Network Performance

Incomplete Beta Function, see Function, Incomplete Beta Industrial, Scientific, and Medical Band (ISM), 60, 197, 198, 241, see also Unlicensed Spectrum & Wireless Fidelity Inference, 82, 89, 328 Information Entropy, 408, 409 Information Sciences Institute (ISI), 420 Information Theory, 22, 39, 64, 220, 298, 408, 409 Input Intercept Point Second Order (IIP2), 160, 169 Third Order (IIP3), 28, 30, 80, 125–140, 160–163, 165–170, 172, 175–181, 184–188, 190–193, 354–358, 361– 364, 368–370, 380, 406 Insertion Loss, 388, 389 Interference, 3, 10, 12, 15, 26–28, 35, 46, 48, 49, 53, 59, 60, 62, 77–79, 81– 83, 87, 96–98, 176, 196, 205, 206, 215–229, 231–233, 235, 236, 241– 245, 248, 250, 251, 253, 255–262, 265–276, 279–292, 308, 309, 328– 330, 332, 334, 336, 361–363, 375, 396, 402, 403, 406 Interference Noise Temperature, 4, 228–234, 237 Interference Radius, see Spectrum Footprint Intermediate Frequency (IF), 17, 20, 21, 123, 345, 353, 355, 389 Intermediate System (IS), 416, 418 Intermodulation, 10–12, 20, 28, 30, 79, 80, 83, 84, 93–96, 124, 159–161, 163–165, 167, 168, 170, 172, 178, 180, 181, 191, 342, 348, 354, 355, 357, 358, 362, 368, 389, 392, see also Intermodulation Distortion, Input Intercept Point Intermodulation Distortion (IMD), 124, 125, 161–163, 165–167, 171, 172, 188, 354, 361, 370 International Standards Organization (ISO), 297– 299 International Telecommunications Union (ITU), 45

Internet, 10, 11, 13, 46, 50, 298–301, 313, 315, 319, 322, 335, 383, 401, 413–421 Internet Protocol, see Protocol, Internet Protocol Internet Protocol Security (IPSEC), 296, 418 Intersymbol Interference (ISI), 36, 38, 97, 389, 408 Jacobson, Van, 314 JAVA, 346 Knowledge Representation Space (KRS), 65 Lambert W Function, see Function, Lambert W Land Mobile Radio (LMR), 43, 46, 249 Late Binding, 314, 315, 328, 384 Learning, 2–4, 58, 64, 328, 330, 404, see also Genetic Algorithms Lehigh University Benchmark, 347 Levy Distribution, see Distribution, Alpha-Stable Link Margin, 8, 31–35, 84, 125, 229, 367 Listen Before Talk (LBT), 235 Local Area Network (LAN), 124, 225, 368, 414, 416, 417 Local Oscillator (LO), 17, 20, 22, 126 Location Awareness, 65, 66 Long Term Evolution (LTE), 62, 216 Low Noise Amplifier (LNA), 17, 19, 20, 28, 78, 79, 88, 94, 95, 104, 126, 159– 161, 166, 169, 170, 184, 190, 192, 193, 368 MANET, see Mobile Ad Hoc Network Mathematica, 274 MATLAB, 13, 120, 121, 145, 274 Media Access Control (MAC), 156, 215, 216, 218–220, 223–225, 234, 266, 281, 283, 296, 297, 368, 376, 393, 406, 414 Meta-materials, 390 Metadata, 315 Microelectromechanical Systems (MEMS), 176, 390 Military Communications, 5, 7, 8, 50, 97, 319, 375–377, 379

Index

Mitola, Joseph, 3, 57, 63, 104, 337 Mixer, 17, 20, 104, 124, 126, 175 Mobile Ad Hoc Network (MANET), 13, 81, 173, 217, 280, 289–291, 301, 302, 304, 305, 380, 402, 403, 417 Modulation, 17, 21–23, 27, 33, 34, 78, 79, 81– 85, 89, 96–98, 123, 160, 195, 266– 268, 271, 286, 291, 316, 332, 335, 345, 356, 366, 393, 394, 403, 408, see also Air Interface, Waveform Monolithic Microwave Integrated Circuits (MMIC), 391 Monopole Antenna, 18 Motorola, 393 Multicast Group, 315 Multihoming, 416, 420 Multipath Effects, 31, 34–38, 89, 90, 96, 254, 335, 336, 389 Multiple Input/Multiple Output (MIMO), 89, 90, 215–219, 224, 335, 344, 346, 347, 382, 407 Name Space, 315 National Radio Astronomy Observatory (NRAO), 102, 103, 115, 133, 135, 148 National Science Foundation (US) (NSF), 66, 102 National Telecommunications and Information Administration (NTIA), 43, 44 Natural Language, 339 Near/Far, 25, 108, 216, 290, 291 Network Density, 2, 10, 11, 60, 80–82, 85, 156, 222, 226, 227, 237, 256–261, 265, 279–292, 353, 355, 361, 401–403 Network Policies, 327, 334 Network Scaling, 2, 221, 228, 251, 279–421 Network Science, 63, 401, 404, 407 Network Topology, 78, 79, 81 Neural Network, 396 Next Generation Communications Program (XG), 60, 65, 67, 69, 196, 235, 329, 374– 380, 397 NEXTEL, 28 Noise Figure, 33, 127 Noise Temperature, 20, 33, 78, 79, 181, 388, 392

455

Noninterfering Basis (NIB), 42 Normal Distribution, see Distribution, Normal Occupancy Threshold, see Threshold Office of Communications (UK) (OFCOM), 328 Omega, see Function, Lambert W Ontological Web Language (OWL), 347 Ontologies, 81–83, 328, 337–340, 347, 403 Open Shortest Path First, see Protocol, Open Shortest Path First Open System Interconnection (OSI) Model, 223, 224, 296–299, 313 Opportunistic Spectrum Access, see Dynamic Spectrum Access Orthogonal Frequency Division Multiplexing (OFDM), 38, 84, 92, 97, 251 Out-of-Band (OOB) Signals, 5, 12, 27, 28, 42, 43, 51, 60, 78, 79, 94, 123, 303, 335, 357, 392, 406 Output Intercept Point, Third Order (OIP3), 163, 368 Overhead, 402 Packet Error Rate (PER), 170 PAR Cube, 6–8 Path Loss, 18, 26, 31, 32, 34, 35, 242–245, 254–257, 279, 290, 291, 308 Peak to Average Ratio (PAR), 38, 97, 408 Peak to Average Usage Ratio (PAUR), 234 Peer to Peer (P2P), 10, 11, 25, 173, 315, 320, 321, 384 Personal Digital Assistant (PDA), 3 Pick Lowest Noise Floor First (PLNFF), 200 Pick Quietest Band First (PQBF), 181 Poisson Distribution, see Distribution, Poisson Policy Conformance Reasoner (PCR), 91, 329, 330, 332–335 Policy Language, 10, 64, 65, 69, 329, 331– 333, 387, 395–397, 403, 404 Power Amplifier, 358, 366 Power Control, 270, 289, 366, 403 Predicate Calculus, 91, 328, 338–340, 396 Preselector, 17, 19, 78–80, 93–96, 104, 123, 125, 130, 136, 137, 139, 146, 159, 163, 164, 171, 175–177, 179, 181,

456

Quantitative Analysis of Cognitive Radio and Network Performance

182, 186, 188, 190, 248, 345, 354, 365, 366, 381 Probability of Detection (PD), 152–154 Probability of False Alarm (PFA), 152–156 Propagation, 3, 5, 31, 32, 95, 202, 203, 205, 207, 208, 213, 227–233, 235, 236, 242–245, 253–257, 259, 262, 269, 270, 274–276, 279–292, 307, 308, 341, 342, 348 Protocol Ad Hoc On-Demand Distance Vector (AODV), 86 Border Gateway (BGP), 415, 416, 418, 420 Dynamic Source Routing (DSR), 86 Internet Protocol (IP), 13, 46, 296, 298, 299, 313, 319, 382, 413–421 Open Shortest Path First (OSPF), 296, 415, 416 Reactive, 86 Transmission Control Protocol (TCP), 296, 298, 382, 419 Voice over Internet (VoIP), 319, 419 Prot´eg´e, 337 Pseudo-Noise Spread Spectrum (PNSS), 25, 265 Public Safety, 5, 6, 8–11, 28, 60, 97, 183, 241, 320, 375, 406, 417, 418 Quadrature Amplitude Modulation (QAM), 21, 84–87, 92 Quadrature Phase Shift Keying (QPSK), 23, 31, 33 Quality Factor (Q), 18, 19, 79, 123, 127, 390 Quality of Service (QoS), 10, 11, 51, 218, 223– 225, 291, 301, 336 Radio Astronomy, 198 Radio Frequency (RF), 15, 18 Radio Frequency Integrated Circuit (RFIC), 22, 38, 68, 159, 379, 381, 387, 390–394 Radio Knowledge Representation Language (RKRL), 65 Radio Resource Management (RRM), 62, 216 Rain Rate, 8, 34 Reasoners, 65, 328, 331–334, 347, 403, 404

Received Signal Strength Indication (RSSI), 345, 348 Receiver Operating Curve (ROC), 153 Reduced Instruction Set Computer (RISC), 394, 395 Reliability, 2, 6–9, 11, 12, 168, 182, 183, 191, 216, 220–222 Request to Send (RTS), 215, 219, 223 Resource Description Framework (RDF), 337 Router, 301, 414–416 Routing, 331, 414, 415, 417–420 Convergence, 418 Exterior, 415, 416, 418 Interior, 415 Table-Driven, 86 Rural Spectrum Environments, 102, 103, 112, 132 Safety of Life, 198, 335, 338 Satellite Communications, 26, 43, 198, 304 SCC 41/IEEE P1900, 404 Secondary Markets, 47, 359 Secondary Spectrum Sharing, see Dynamic Spectrum Access Security, 10, 11, 328, 331 Selectivity, 388–391 Semantic Signal Processing Language (SSPL), 337 Semantics, 10, 65, 82, 83, 403 Sensing, 3, 48, 49, 61–64, 66, 67, 77, 79, 83, 88–91, 93, 95, 96, 108, 143, 144, 151, 152, 155, 208, 210, 218, 227, 228, 235, 250, 251, 256, 259, 262, 334, 340, 343, 345, 346, 348, 402, 407, 408 Service Set Identifier (SSID), 200 Shannon-Hartley, 22, 23, 26, 127, 266, 267, 269, 286, 298, 366, 408, 421 Shared Spectrum Corporation (SSC), 65, 102, 375–377 Signal Processing, 59 Signal to Interference and Noise Ratio (SINR), 30, 243–245 Signal to Noise Ratio (SNR), 108 Silicon Germanium (SiGe), 391 Skype, 419

Index

Slotted Access, 26 Social Networks, 63, 319, 322, 407 Software Defined Radio (SDR), 39, 327, 395, 404 Software Defined Radio Forum (SDRF), 59, 64, 69 Spectral Power Density (SPD), 226, 284, 286, 289 Spectrum Brokers, 359 Spectrum Density, 3, 5, 10, 59, 82, 87, 144, 159, 242, 248–250, 252, 253, 256, 259, 261, 266, 279–292, 361, 401, 403, 406 Spectrum Efficiency, 1, 2, 47, 267–269, 361 Spectrum Footprint, 12, 42, 94, 97, 195, 235, 242, 265–276, 291 Spectrum Management, 28, 42–44, 58, 94, 195, 196, 225, 226, 229, 241, 328, 330, 362, 366, 401 Spectrum Management and Regulation, 4, 11, 41–43, 46, 48–51, 57, 59–61, 67, 102, 193, 215, 216, 218, 219, 222, 225, 226, 229, 241, 328, 332, 333, 392, 406 Spectrum Opportunity, 42, 59, 91, 92, 101– 121, 123, 130, 151, 155, 220, 362 Spectrum Outage Probability (SOP), 250, 251, 257 Spectrum Policies, 53, 87, 88, 196, 198, 327, 328, 338, 339, 347 Spectrum Policy Task Force, 4, 47, 228 Spectrum Pooling, 60, 221, 234–236, 245– 249, 261, 262, 282, 361, 363–366 Spectrum Reuse, 265–276 Spectrum Underlay, 48, 82 Spread Spectrum, 25, 48, 108, 225, 265 Squatter’s Rights, 50, 51 SRI International (SRI), 65 Stability, 404–407 Standards Coordinating Committee (SCC), 404 Superheterodyne Receiver, 17–21 System Strategy Reasoner (SSR), 329, 330, 332–334 Television (TV), 34, 46, 51, 52, 57–62, 67, 69, 169, 176, 335, 374, 406

457

Tesla, Nichola, 1 Thermal Load, 357 Threshold, 105, 108, 151–156, 179, 374 Tilera Processor, 395 Time Division Duplex (TDD), 25, 49, 51, 173, 265 Time Division Multiple Access (TDMA), 225, 266, 346, 376 Topology, 10, 11, 42, 68, 79, 81, 85–87, 92, 182, 220, 295, 300, 302, 314, 331, 416, 418, 419 Tragedy of the Commons, 97, 270 Transistor, 1, 391 Transmission Control Protocol, see Protocol, Transmission Control Protocol Transport Layer, 223, 224, 296 Trunking Networks, 10 Tsunami (2004), 5, 319 Ultra High Frequency (UHF), 389 Ultrawideband (UWB), 4, 25, 48, 62, 82, 83, 216, 225, 228 Universal Quantification, 338–340 Unlicensed Spectrum, 4, 52, 60, 77, 197, 205, 212, 241, see also Industrial, Scientific, and Medical Band & Wireless Fidelity Urban Spectrum Environments, 102, 103, 112, 132, 133, 147, 148 Use Case, 9–11, 417 V-Chip, 335 Vacuum Tube, 1 Varactor, 176, 389, 390, see also Filter, Varactor Very High Frequency (VHF), 34, 268, 389 Virtual Private Network (VPN), 296 Voice over Internet Protocol, see Protocol, Voice over Internet (VoIP) Waveform, 1, 24, 25, 27, 78, 79, 82–85, 225, 251, 265, 270, 273, 393–395, 408, see also Air Interface, Modulation Wavelength, 18, 19, 32, 97 Web Ontology Language (OWL), 337

458

Quantitative Analysis of Cognitive Radio and Network Performance

White Space, 4, 27, 51, 52, 57–62, 67, 69, 96, 345, 374 Wireless Fidelity (Wi-Fi), 4, 6, 7, 9–11, 25, 60, 61, 200, 210, 250, 296, 298, 302, 356, 367, 368, 373, 382, 391, 393, 401, 416–419, see also Industrial, Scientific, and Medical Band & Unlicensed Spectrum, Wireless Local Area Network (WLAN), 201, see also Wireless Fidelity (Wi-Fi)

Wireless Network after Next (WNaN), 60, 63, 67, 68, 92, 304, 305, 317, 318, 340, 346, 374, 378–382, 393 Wireless Regional Area Networks (WRAN), 5, 69, see also IEEE 802.22 World Radio Congress, 45 Worldwide Interoperability for Microwave Access (WiMAX), 234 Zero-IF Receiver, 22