This book gives comprehensive and balanced coverage of the principles of cognitive radio communications, cognitive networks, and details of their implementation, including the latest developments in the standards and spectrum policy. Case studies, end-of-chapter questions, and descriptions of various platforms and test beds, together with sample code, give hands-on knowledge of how cognitive radio systems can be implemented in practice. Extensive treatment is given to several standards, including IEEE 802.22 for TV White Spaces and IEEE SCC41.
Written by leading people in the field, both at universities and major industrial research laboratories, this tutorial text gives communications engineers, R&D engineers, researchers, undergraduate and post graduate students a complete reference on the application of wireless communications and network theory for the design and implementation of cognitive radio systems and networks.
- Each chapter is written by internationally renowned experts, giving complete and balanced treatment of the fundamentals of both cognitive radio communications and cognitive networks, together with implementation details
- Extensive treatment of the latest standards and spectrum policy developments enables the development of compliant cognitive systems
- Strong practical orientation - through case studies and descriptions of cognitive radio platforms and testbeds - shows how real world cognitive radio systems and network architectures have been built
- Additional materials, slides, solutions to end-of-chapter problems, and sample codes, are available at www.elsevierdirect.com/companions
Alexander M. Wyglinski is an Assistant Professor of Electrical and Computer Engineering at Worcester Polytechnic Institute (WPI), Director of the WPI Limerick Project Center, and Director of the Wireless Innovation Laboratory (WI Lab).
Maziar Nekovee leads cognitive radio research at BT (British Telecom) and is also involved in leading a number of large EU and International collaborative R&D projects on cognitive radio networks and secondary/dynamic spectrum access.
Y. Thomas Hou is an Associate Professor of Electrical and Computer Engineering at Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, VA, USA.
- Each chapter is written by internationally renowned experts, giving complete and balanced treatment of the fundamentals of both cognitive radio communications and cognitive networks, together with implementation details
- Extensive treatment of the latest standards and spectrum policy developments enables the development of compliant cognitive systems
- Strong practical orientation - through case studies and descriptions of cognitive radio platforms and testbeds - shows how real world cognitive radio systems and network architectures have been built
- Additional materials, slides, solutions to end-of-chapter problems, and sample codes, are available at www.elsevierdirect.com/companions
Cognitive Radio Communications and Networks gives comprehensive and balanced coverage of the principles of cognitive radio communications, cognitive networks, and details of their implementation, including the latest developments in the standards and spectrum policy. Case studies, end-of-chapter questions, and descriptions of various platforms and test beds, together with sample code, give hands-on knowledge of how cognitive radio systems can be implemented in practice. Extensive treatment is given to several standards, including IEEE 802.22 for TV White Spaces and IEEE SCC41 Written by leading people in the field, both at universities and major industrial research laboratories, this tutorial text gives communications engineers, R&D engineers, researchers, undergraduate and post graduate students a complete reference on the application of wireless communications and network theory for the design and implementation of cognitive radio systems and networks Each chapter is written by internationally renowned experts, giving complete and balanced treatment of the fundamentals of both cognitive radio communications and cognitive networks, together with implementation details Extensive treatment of the latest standards and spectrum policy developments enables the development of compliant cognitive systems Strong practical orientation - through case studies and descriptions of cognitive radio platforms and testbeds - shows how real world cognitive radio systems and network architectures have been built Alexander M. Wyglinski is an Assistant Professor of Electrical and Computer Engineering at Worcester Polytechnic Institute (WPI), Director of the WPI Limerick Project Center, and Director of the Wireless Innovation Laboratory (WI Lab) Each chapter is written by internationally renowned experts, giving complete and balanced treatment of the fundamentals of both cognitive radio communications and cognitive networks, together with implementation details Extensive treatment of the latest standards and spectrum policy developments enables the development of compliant cognitive systems Strong practical orientation - through case studies and descriptions of cognitive radio platforms and testbeds - shows how "e;real world"e; cognitive radio systems and network architectures have been built
Front Cover 1
Cognitive Radio Communications and Networks: Principles and Practice 2
Copyright 3
Dedication 4
Contents 6
Preface 19
About the Editors 22
Chapter 1: When radio meets software 24
1.1 Introduction 24
1.2 Software-Defined Radio 25
1.2.1 What Is Software-Defined Radio? 25
1.2.2 Evolution of Software-Defined Radio 27
1.3 Cognitive Radio 29
1.3.1 What Is Cognitive Radio? 29
1.3.2 Evolution of Cognitive Radio 31
1.4 Key Applications 32
1.4.1 Interoperability 32
1.4.2 Dynamic Spectrum Access 33
1.5 Book Organization 35
Theme 1: Cognitive radiocommunicationtechniques andalgorithms 36
Chapter 2: Radio frequency spectrum andregulation 38
2.1 Introduction
38
2.2 Spectrum: Nature’s Communication Highway 38
2.2.1 Physical Characteristics of Spectrum 39
2.2.2 Implications for Communication Applications 42
2.3 Regulatory History and Successes 42
2.3.1 Objectives and Philosophy 42
2.3.2 Early History and Success 43
2.4 Emerging Regulatory Challenges and Actions 44
2.4.1 Era of Increasing Regulatory Challenges 46
2.4.2 Allocation, Reallocation, and Optimization 47
2.4.3 Regulatory Actions 48
2.4.4 Spectrum Task Forces and Commissions 49
2.5 Regulatory Issues of Cognitive Access 49
2.5.1 Should a Regulator Allow Cognitive Access? 49
2.5.2 How to Determine the Rules of Entry 51
2.5.3 Regulatory Implications of Different Methods of Cognition 52
2.5.4 Regulatory Developments to Date 53
2.6 Spectrum Measurements and Usage 54
2.6.1 Early Spectrum Occupancy Studies 54
2.6.2 Snapshot Studies 55
2.6.3 Spectrum Observatory 55
2.6.4 Spectral Sensor Arrays 57
2.7 Applications for Spectrum Occupancy Data 58
2.7.1 Regulatory Guidance 59
2.7.2 Wireless Systems and Device Design Opportunities 60
2.7.3 Wireless Communications and Data Service Providers 60
2.7.4 Societal Value 60
2.8 Chapter Summary and Further Readings 61
2.9 Problems
61
Chapter 3: Digital communication
64
3.1 Introduction 64
3.2 Data Transmission 65
3.2.1 Fundamental Limits 65
3.2.2 Sources of Transmission Error 66
3.3 Digital Modulation Techniques 68
3.3.1 Representation of Signals 69
3.3.2 Euclidean Distance between Signals 70
3.3.3 Decision Rule 71
3.3.4 Power Efficiency 71
3.3.5 M-ary Phase Shift Keying 72
3.3.6 M-ary Quadrature Amplitude Modulation 73
3.4 Probability of Bit Error 74
3.4.1 Derivation of Probability of Bit Error 75
3.4.2 Probability of Bit Error of M-ary Phase Shift Keying 80
3.5 Multicarrier Modulation 80
3.5.1 Basic Theory 81
3.5.2 Orthogonal Frequency Division Multiplexing 86
3.5.3 Filter Bank Multicarrier Systems 89
3.6 Multicarrier Equalization Techniques 90
3.6.1 Interference in Multicarrier Systems 90
3.6.2 Distortion Reduction 91
3.6.3 Optimal Single-Tap Per-Tone Equalization for OFDM Systems 93
3.6.4 Frequency-Domain Equalizers for Multicarrier Systems 95
3.7 Intersymbol Interference 95
3.7.1 Peak Interference/Peak Distortion 97
3.7.2 Chernoff Bound 97
3.8 Pulse Shaping 98
3.8.1 Nyquist Pulse Shaping Theory 99
3.8.2 Nyquist Frequency-Domain No ISI Criterion 102
3.9 Chapter Summary and Further Readings
103
3.10 Problems
103
Chapter 4: Spectrum sensing and identification 108
4.1 Introduction 108
4.2 Primary Signal Detection 109
4.2.1 Energy Detector 111
4.2.2 Cyclostationary Feature Detector 115
4.2.3 Matched Filter 115
4.2.4 Cooperative Sensing 116
4.2.5 Other Approaches 117
4.3 From Detecting Primary Signals To Detecting Spectrum Opportunities 118
4.3.1 Definition and Implications of Spectrum Opportunity 118
4.3.2 Spectrum Opportunity Detection 120
4.4 Fundamental Trade-Offs: Performance Versus Constraint 124
4.4.1 MAC Layer Performance Measures 124
4.4.2 Global Interference Model 125
4.4.3 Local Interference Model 126
4.5 Fundamental Trade-Offs: Sensing Accuracy Versus Sensing Overhead 129
4.6 Chapter Summary and Further Readings 131
4.7 Problems
132
Chapter 5: Spectrum access and sharing 136
5.1 Introduction 136
5.2 Unlicensed Spectrum Sharing 140
5.3 Licensed Spectrum Sharing 142
5.4 Secondary Spectrum Access 147
5.5 Non-Real-Time SSA 148
5.6 Real-Time SSA
148
5.6.1 Negotiated Access 149
5.6.2 Is Quality of Service Provisioning Possible in a Shared Band? 151
5.6.3 Opportunistic Access 156
5.6.4 Overlay Approach 157
5.6.5 Underlay Approach 163
5.7 Chapter Summary
168
5.8 Problems
169
Chapter 6: Agile transmission techniques 172
6.1 Introduction
172
6.2 Wireless Transmission For Dynamic Spectrum Access 173
6.2.1 Spectrum Pooling 174
6.2.2 Underlay and Overlay Transmission 174
6.3 Noncontiguous Orthogonal Frequency Division Multiplexing 177
6.4 NC-OFDM-Based Cognitive Radio: Challenges and Solutions 178
6.4.1 Interference Mitigation 179
6.4.2 FFT Pruning for NC-OFDM 188
6.4.3 Peak-to-Average Power Ratio Problem in NC-OFDM 190
6.5 Chapter Summary and Further Readings 197
6.6 Problems
198
Chapter 7: Reconfiguration, adaptation,and optimization 200
7.1 Introduction
200
7.2 Adaptation Engine 201
7.3 Operating Parameters 202
7.3.1 Transmission Parameters 202
7.3.2 Environmental Measurements 203
7.4 parameter relationships 205
7.4.1 Single Radio Performance Objectives 206
7.4.2 Multiple Objective Goals 208
7.5 Cognitive Adaptation Engines 210
7.5.1 Expert Systems 211
7.5.2 Genetic Algorithms 212
7.5.3 Case-Based Reasoning Systems 214
7.6 Chapter Summary 219
7.7 Problems
220
Theme 2: Cognitive radionetwork theory 222
Chapter 8: Fundamentals ofcommunication networks 224
8.1 Introduction
224
8.2 Architecture and Building Blocks 224
8.2.1 Protocol Architecture 224
8.2.2 Switching Technologies 226
8.2.3 Encapsulation and Multiplexing 227
8.2.4 Naming and Addressing 228
8.2.5 Multiple Access 229
8.2.6 Routing and Forwarding 229
8.2.7 Congestion Control and Flow Control 229
8.2.8 Error Control 230
8.3 New Challenges in Wireless Networks 231
8.3.1 Wireless Transmissions 231
8.3.2 Mobility 232
8.3.3 Energy Efficiency 233
8.4 Mobility Modeling 233
8.4.1 Mobility Models 233
8.4.2 The Random Waypoint Model 234
8.4.3 Perfect Simulation 236
8.5 Power Control and Multiuser Diversity 237
8.6 Multiple Access Schemes 240
8.6.1 Polling 241
8.6.2 ALOHA and Slotted ALOHA 243
8.6.3 CSMA 244
8.6.4 CSMA / CA 247
8.7 Routing, Energy Efficiency, and Network Lifetime 250
8.8 Congestion Control in Wireless Networks 252
8.9 Cross-Layer Design and Optimization 254
8.10 Chapter Summary
256
8.11 Problems
256
Chapter 9: Cognitive radio network architectures 258
9.1 Introduction
258
9.2 Cognitive Radio Network Architectures 259
9.2.1 Cognitive Resource Manager Framework 260
9.2.2 Architectures for Spectrum Sensing 267
9.2.3 Network Optimization through Utilities 270
9.2.4 Value of Perfect Information 272
9.2.5 Policy Support as a Part of the Architecture 273
9.2.6 Spectrum Brokering Services 274
9.2.7 Information Modeling 275
9.3 Topology-Aware CRN Architectures 276
9.3.1 Statistical Characterization of Node Locations 277
9.3.2 Spatial Statistics of Spectrum Usage 280
9.3.3 Applications and Discussion 281
9.4 Publish-Subscribe CRN Architecture 281
9.5 Chapter Summary
282
9.6 Problems
282
Chapter 10: User cooperative communications 284
10.1 Introduction
284
10.1.1 Diversity 285
10.1.2 User Cooperation and Cognitive Systems 286
10.1.3 Chapter Preview 287
10.2 Relay Channels
287
10.2.1 Introduction 287
10.2.2 A General Three-Node Relay Channel 290
10.2.3 Wireless Relay Channel 296
10.3 User Cooperation in Wireless Networks 306
10.3.1 Introduction 306
10.3.2 Two-User Cooperative Network 307
10.3.3 Cooperative Wireless Network 311
10.4 Multihop Relay Channel 320
10.5 Chapter Summary and Further Readings 327
10.6 Problems
327
Chapter 11: Information theoreticallimits on cognitive radio networks 330
11.1 Introduction
330
11.1.1 The Rise and Importance of Cognitive Networks 331
11.1.2 Types of Cognitive Behavior 332
11.1.3 Chapter Preview 334
11.2 Information Theoretic Basics
335
11.2.1 Communications Channels 335
11.2.2 Information Theoretic Metrics of Interest 337
11.2.3 Classic Channels 339
11.3 Interference-Avoiding Behavior: Spectrum Interweave 341
11.4 Interference-Controlled Behavior: Spectrum Underlay 343
11.4.1 Underlay in Small Networks: Achievable Rates 343
11.4.2 Underlay in Large Networks: Scaling Laws 344
11.5 Interference-Mitigating Behavior: Spectrum Overlay 347
11.5.1 Opportunistic Interference Cancellation 347
11.5.2 Asymmetrically Cooperating Cognitive Radio Channels 348
11.6 Chapter Summary
355
11.7 Problems
355
Chapter 12: Cross-layer optimization formultihop cognitive radionetworks 358
12.1 Introduction 358
12.2 Mathematical Models at Multiple Layers 360
12.2.1 Scheduling and Power Control 361
12.2.2 Routing 365
12.3 A Case Study: The Throughput Maximization Problem 367
12.3.1 Problem Formulation 367
12.3.2 Solution Overview 368
12.3.3 Linear Relaxation 370
12.3.4 Local Search Algorithm 373
12.3.5 Selection of Partition Variables 374
12.4 Numerical Results for the Throughput Maximization Problem 375
12.4.1 Simulation Setting 375
12.4.2 Results and Observations 376
12.5 Chapter Summary
385
12.6 Problems
385
Theme 3: Applications,standards, andimplementations ofcognitive radio 388
Chapter 13:
390
13.1 Introduction
390
13.2 Defining CR History, Applications, and Related Concepts
391
13.2.1 A Brief History of Elastic Spectrum Management 391
13.2.2 A View of Wireless Network Futurists 394
13.2.3 Ambiguity in CR Definitions 395
13.2.4 A Glossary of Cognitive Radio Definitions 398
13.2.5 A Generalized Definition of Cognitive Radio Network 399
13.2.6 Concepts Related to Spectrum Management 399
13.2.7 Concepts Related to Computational Platforms 400
13.3 CR Terminology Standardization 402
13.3.1 General Overview 402
13.3.2 IEEE 1900.1 403
13.3.3 IEEE 1900.2 404
13.3.4 IEEE 1900.3 404
13.3.5 IEEE 1900.4 405
13.3.6 IEEE 1900.5 405
13.3.7 IEEE 1900.6 406
13.3.8 Related Standardization Efforts 406
13.3.9 Results and Roadmap of IEEE SCC41 407
13.4 Chapter Summery
408
13.5 Problems
408
Chapter 14: Cognitive radio for broadband wireless accessin TV bands: The IEEE 802.22 standards 410
14.1 Introduction
410
14.1.1 Cognitive Radios 411
14.1.2 Regulatory Scenario for TV White Space 411
14.1.3 Dynamic Spectrum Access Models 412
14.2 Overview of IEEE 802.22 Standard 413
14.2.1 Applications 414
14.2.2 Reference Architecture 414
14.3 IEEE 802.22 Physical Layer 416
14.3.1 Preamble, Control Header, and MAP Definition 416
14.3.2 CBP Packet Format 419
14.3.3 Channel Coding and Modulation Schemes 420
14.3.4 Transmit Power Control 421
14.3.5 RF Mask 421
14.4 IEEE 802.22 Medium-Access Control Layer 422
14.4.1 Superframe and Frame Structures 422
14.4.2 Incumbent Detection and Notification Support 425
14.4.3 Multichannel Operation 426
14.4.4 Synchronization 427
14.4.5 Self-Coexistence 428
14.4.6 Quality-of-Service Support 431
14.4.7 Spectrum Management Model 432
14.4.8 Spectrum Manager 433
14.4.9 Spectrum Sensing Function 434
14.4.10 Incumbent Database Support 434
14.5 Spectrum Sensing
435
14.5.1 Incumbent Protection Radius 435
14.5.2 Sensing Algorithms 439
14.6 Other Standardization Activities 450
14.6.1 IEEE 802.22.1 Standard 450
14.6.2 Other Related Standards: IEEE 802.16h, SCC41 450
14.7 Chapter Summary and Future Directions 451
14.8 Problems
452
Chapter 15: Cognitive radio network security 454
15.1 Introduction
454
15.1.1 Overview of Security Threats to Incumbent Coexistence 454
15.1.2 Overview of Security Threats to Self-Coexistence 456
15.1.3 Radio Software Security Threats 457
15.2 Primary-User Emulation Attacks 458
15.2.1 Spectrum Sensing in Hostile Environments 458
15.2.2 Classification of PUE Attacks 459
15.2.3 Noninteractive Localization of Primary Signal Transmitters 460
15.2.4 Simulation Results 464
The Effects of PUE Attacks 464
Simulation on the Localization System 466
Simulation Setting and Objectives 466
The Case of a Single Transmitter 467
15.2.5 Related Research 469
15.3 Robust Distributed Spectrum Sensing 469
15.3.1 Technical Background 470
15.3.2 Weighted Sequential Probability Ratio Test 472
15.3.3 Simulations 474
15.4 Security Vulnerabilities in Ieee 802.22 479
15.4.1 The 802.22 Air Interface 480
15.4.2 An Overview of the IEEE 802.22 Security Sublayer 483
15.4.3 Security Vulnerabilities in Coexistence Mechanisms 485
15.5 Security Threats to the Radio Software 486
15.6 Problems
488
Chapter 16: Public safety and cognitive radio 490
16.1 Introduction
490
16.1.1 Requirements 491
16.1.2 Commercial Wireless Communication Networks 493
16.1.3 Economic Value of the Spectrum 493
16.1.4 Benefits of Cognitive Radio 495
16.2 Standards for Public Safety Communication 496
16.2.1 TETRA 496
16.2.2 C2000 499
16.3 Application Of Cognitive Radio 500
16.3.1 The Firework Disaster in The Netherlands 500
16.3.2 Bandwidth Requirements 501
16.3.3 Spectrum Organization 502
16.3.4 Propagation Conditions 504
16.3.5 White Space Assessment 505
16.3.6 System Spectral Efficiency 508
16.3.7 Antijamming 508
16.4 Chapter Summary
509
16.5 Problems
510
Chapter 17: Auction-based spectrum markets in cognitive radio networks 512
17.1 Introduction
512
17.1.1 Dynamic Spectrum Micro-Auctions 513
17.1.2 The Role of Cognitive Radios 514
17.2 Rethinking Spectrum Auctions 514
17.3 On-Demand Spectrum Auctions 516
17.3.1 Bidding Format: Piecewise Linear Price-Demand Bids 516
17.3.2 Pricing Models 516
17.3.3 Fast Auction Clearing by Linearizing the Interference Constraints 517
17.4 Economically Robust Spectrum Auctions 517
17.4.1 Spectrum Allocation 520
17.4.2 Winner Pricing 520
17.4.3 Supporting Other Bidding Formats 520
17.4.4 Supporting Different Auction Objectives 521
17.4.5 VERITAS Performance and Complexity 521
17.5 Double Spectrum Auctions for Multiparty Trading 522
17.5.1 Grouping Buyers 524
17.5.2 Determining Winners 525
17.5.3 Pricing 525
17.5.4 TRUST Performance and Complexity 526
17.6 Chapter Summary and Further Reading 526
17.7 Problems
528
Chapter 18: GNU radio for cognitive radio experimentation 530
18.1 Introduction
530
18.1.1 Introduction to GNU Radio 530
18.1.2 The Software 531
18.1.3 The Hardware 532
18.1.4 GNU Radio Resources 532
18.2 Analog Receiver
533
18.2.1 The First Line 534
18.2.2 Importing Necessary Modules 534
18.2.3 The Initialization Function 535
18.2.4 Constructing the Graph 536
18.3 Digital Transmitter
538
18.3.1 Building the Radio 539
Modulating the Data 540
Setting up the USRP 541
18.3.2 Running the Transmitter 543
18.4 Digital Receiver
545
18.4.1 Building the Radio 546
18.4.2 Creating the User Interface 550
18.4.3 Running the Receiver 554
18.5 Cognitive Transmitter 555
18.5.1 Building the Radio 555
18.5.2 Running the Transmitter 557
18.6 Chapter Summary
559
18.7 Problems
559
Chapter 19: Cognitive radio platforms and testbeds 562
19.1 Introduction
562
19.2 Cognitive Radio Platform Based on Berkeley Emmulation Engine 564
19.2.1 Test Bed Architecture 564
19.2.2 Supported Configurations 568
19.2.3 Case Study: Spectrum Sensing 570
19.2.4 Lessons Learned 575
19.3 Motorola 10 Mhz–4 Ghz Cmos-Based,Experimental Cognitive Radio Platform 577
19.3.1 Introduction 577
19.3.2 Integrated Radio Front End: The RFIC 577
19.3.3 Experimental Cognitive Radio Platform 582
19.3.4 Case Study: Cyclostationary Analysis 587
19.3.5 Lessons Learned 588
19.4 The Maynooth Adaptable Radio System 589
19.4.1 Introduction 589
19.4.2 Design Motivation 592
19.4.3 Experiments and Use Cases 600
19.4.4 Lessons Learned 603
19.4.5 Future Plans 605
19.5 Chapter Summary
605
19.6 Problems
606
Chapter 20: Cognitive radio evolution 610
20.1 Introduction
610
20.1.1 Organization 611
20.2 Cognitive Radio Architectures 612
20.2.1 Dynamic Spectrum Access 614
20.2.2 The Haykin Dynamic Spectrum Architecture 615
20.2.3 The Ideal CRA 617
20.2.4 Networking and CRA Evolution 618
20.3 Architecture Evolution and Use Case Evolution 620
20.3.1 Product Differentiation 620
20.3.2 Protocol Stacks 621
20.3.3 OA& M
20.3.4 Location Awareness 622
20.3.5 Spectrum Awareness 622
20.3.6 Spectrum Auctions 623
20.3.7 User Expectations 623
20.3.8 First Responder Situation Awareness 625
20.3.9 Commercial Sentient Spaces 625
20.4 Sensory Perception in the Evolving Cra 626
20.4.1 Machine Vision 627
20.4.2 Human Language and Machine Translation 627
20.4.3 Situation Perception Architectures 630
20.5 Quality of Information 631
20.5.1 Quantity 632
20.5.2 Quality: Precision and Recall 632
20.5.3 Quality: Accuracy 633
20.5.4 Timeliness 633
20.5.5 Quality: Validity 633
20.5.6 Quality: Level of Detail 634
20.6 Cognitive Radio Policy Languages 634
20.6.1 What Is a Policy Language? 634
20.6.2 Policy Language Needs 635
20.6.3 What Is Language? 636
20.6.4 Cognitive Linguistics for CRPLA 637
20.6.5 CRPLA Evolution 638
20.7 Challenges And Opportunities 640
20.8 Chapter Summary
641
Appendices: GNU radioexperimentation 642
Appendix A: Essential linux commands 644
Appendix B: GNU radio installation guide 646
B.1 Install Dependencies
646
B.2 Install an SVN Client 647
B.3 Install
648
B.4 Set up USRP 651
B.5 Test USRP 652
B.6 General Installation Notes 654
Appendix C: Universal software radio
655
C.1 The Main Elements on the Usrp Board 655
C.2 Data Flow on the USRP 657
Appendix D: GNU radio python program structure 664
Appendix F: Digital transmitter code 669
Appendix G: Digital receiver code 673
Appendix H: Adaptive transmitter code 680
References 688
Index
728
When radio meets software
Alexander M. Wyglinski1; Maziar Nekovee2; Y. Thomas Hou3 1 Worcester Polytechnic Institute, United States
2 BT Research and University College London, United Kingdom
3 Virginia Polytechnic Institute and State University, United States
1.1 INTRODUCTION
Data communication networks are a vital component of any modern society. They are used extensively in numerous applications, including financial transactions, social interactions, education, national security, and commerce. In particular, both wired and wireless devices are capable of performing a plethora of advanced functions that support a range of services, such as voice telephony, web browsing, streaming multimedia, and data transfer. With the rapid evolution of microelectronics, wireless transceivers are becoming more versatile, powerful, and portable. This has enabled the development of software-defined radio (SDR) technology, where the radio transceivers perform the baseband processing entirely in software: modulation/demodulation, error correction coding, and compression.
Since its introduction in 1991, SDR has been defined as a radio platform of which the functionality is at least partially controlled or implemented in software. Consequently, any waveform defined in the memory of the SDR platform can be employed on any frequency [1]. Although initially constrained by the conversion process between the analog and digital signaling domains, the emergence of cheap high-speed digital-to-analog converters (DACs) and analog-to-digital converters (ADCs) has brought the ideal SDR concept of an entirely software communication system implementation (including radio frequency functionality) closer to a reality.
Wireless devices that can be described as SDR have in fact been around for several decades. They were initially employed in military applications before finding applications in the commercial sector. Military programs such as SPEAKeasy sought to enable communication and interoperability between several military standards [2]. Although ambitious, the SPEAKeasy project did produce a functional prototype, even though the design choices involved in programming waveforms using low-level assembly language meant that the software was not compatible with newer processors. Furthermore, in terms of portability, the Phase I prototype of SPEAKeasy was large enough to fit into the back of a truck [3].
One of the first significant commercial introductions of SDR platforms was the Vanu AnywaveTM software radio base station, which incorporated multiple cellular access standards into a simple SDR implementation. Since the cellular standards are based in software, they can be changed “on the fly” to adapt to different user needs of each cell, rather than replacing the radio frequency (RF) hardware, which can be a prohibitively expensive upgrade. Furthermore, new standards can be uploaded to the SDR platform for immediate deployment in a cellular region [4]. To increase the effectiveness and improve the aging process of an SDR platform, most developers seek to use portable code for their software, reusable components that can work under different waveform configurations, and generic hardware that can be easily upgraded [5].
Given the ease and speed of programming baseband operations in an SDR platform, this technology is considered to be a prime candidate for numerous advanced networking applications and architectures that were unrealizable only several years ago. An SDR platform that can rapidly reconfigure operating parameters based on changing requirements and conditions and through a process of cognition is known as cognitive radio [6]. The term cognitive radio (CR) was first defined by Joseph Mitola III [7]. According to Mitola, CR technology is the “intersection of personal wireless technology and computational intelligence,” where CR is defined as “a really smart radio that would be self-aware, RF-aware, user-aware, and that would include language technology and machine vision along with a lot of high-fidelity knowledge of the radio environment” [7]. Cognitive radio clearly goes hand in hand with SDR; together, they can achieve functionality considered impossible only a decade ago. Consequently, before continuing any further with respect to CR, we first provide an overview of SDR technology.
1.2 SOFTWARE-DEFINED RADIO
1.2.1 What Is Software-Defined Radio?
Before describing what SDR does, it is useful to review the design of a conventional digital radio. Figure 1.1 shows a block diagram of a generic digital radio [8], which consists of five sections:
The antenna section, which receives (or transmits) information encoded in radio waves.
The RF front-end section, which is responsible for transmitting/receiving radio frequency signals from the antenna and converting them to an intermediate frequency (IF).
The ADC/DAC section, which performs analog-to-digital/digital-to-analog conversion.
The digital up-conversion (DUC) and digital down-conversion (DDC) blocks, which essentially perform modulations of the signal on the transmitting path and demodulation of the signal on the receiving path.
The baseband section, which performs operations such as connection setup, equalization, frequency hopping, coding/decoding, and correlation, while also implementing the link layer protocol.
The DDC/DUC and baseband processing operations require large computing power, and in a conventional digital radio are implemented in dedicated hardware. In programmable digital radio (PDR) systems baseband operations and link layer protocols are implemented in software while the DDC/DUC functionality is performed using application-specific integrated circuits (ASICs).
Software-defined radio refers to technologies wherein these functionalities are performed by software modules running on field programmable gate arrays (FPGAs), digital signal processors (DSP), general-purpose processors (GPP), or a combination thereof. This enables programmability of both DDC/DUC and baseband processing blocks. Hence, operation characteristics of the radio, such as coding, modulation type, and frequency band, can be changed at will, simply by loading a new software. Also multiple radio devices using different modulations can be replaced by a single radio device that can perform the same task.
If the AD/DA conversion can be pushed further into the RF block, the programmability can be extended to the RF front end and an ideal software radio can be implemented. However, there are a number of challenges in the transition from hardware radio to software (-defined) radio. First, transition from hardware to software processing results in a substantial increase in computation, which in turn results in increased power consumption. This reduces battery life and is one of the key reasons why software-defined radios have not been deployed yet in end-user devices, but rather in base stations and access points, which can take advantage of external power resources.
Second, the question where the AD/DA conversion can be performed determines what radio functions can be done in software and hence how reconfigurable a radio can be made. The ultimate goal for software radio is to move the AD/DA conversion as close as possible to the antenna so that all signal processing can be done digitally. However, two technical limitations make it currently infeasible to the AD/DA conversion at the antenna. First, digitization of the RF signal requires the incoming signal to be sampled at least at a rate that is determined by the Nyquist frequency. Additionally, the higher the data rate of the signal, the higher the resolution required to capture the information. Taken together, this means that high-bandwidth, high-frequency RF transmissions require very high sampling rates.
The ability to support very high sampling rates, which is especially critical with the use of high-frequency signals in the gigahertz range, limits the range of what can be digitized. To give an example, the typical channels used by an 802.11 WiFi device are 20 MHz wide. To assure that the full 20 MHz is presented to the modem without distortion, it is not unusual for ADC to digitize 40 MHz or so of signal bandwidth. To capture 40 MHz of analog signal bandwidth set by the IF filters without aliasing artifacts, the ADC will probably sample the signal at a rate above 80 million samples per second (Msps). Indeed, it is only recently that sufficiently fast DSPs and wideband AD/DA chipsets have become available at affordable cost to make it feasible to contemplate AD conversions of the IF rather than the baseband signal.
SDR is currently used to build radios that support multiple interface technologies (e.g., CDMA, GSM, and WiFi) with a single modem by reconfiguring it in software. However, SDR modems are expensive, since they typically entail programmable devices like FPGAs, as opposed to the mass-produced, single-purpose ASICs used in most consumer devices today (and are key enablers for low-cost handsets). Even today’s multimode devices tend to just have multiple ASICs (or multiple cores...
| Erscheint lt. Verlag | 13.11.2009 |
|---|---|
| Sprache | englisch |
| Themenwelt | Sachbuch/Ratgeber |
| Technik ► Elektrotechnik / Energietechnik | |
| Technik ► Nachrichtentechnik | |
| ISBN-10 | 0-08-087932-2 / 0080879322 |
| ISBN-13 | 978-0-08-087932-1 / 9780080879321 |
| Haben Sie eine Frage zum Produkt? |
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