Title Details

Dr. David Grace, University of York, UK
David Grace is Head of Communications Research Group and Co-Director of York-Zhejiang Lab for Cognitive Radio and Green Communications. He received his MEng and DPhil degrees from York in 1993 and 1999 respectively. David's current research interests include cognitive radio and green communications, specifically spectrum assignment aspects, and cognitive networking.

Dr. Honggang Zhang, Zhejiang University, China
Honggang Zhang is a Full Professor at the Department of Information Science and Electronic Engineering, Zhejiang University, China. He received the Ph.D. degree in Electrical Engineering from Kagoshima University, Japan, in March 1999. Prior to that, he received the Bachelor of Engineering and Master of Engineering degrees, both in Electrical Engineering, from Huazhong University of Science & Technology (HUST), China, in 1989, and Lanzhou University of Technology, China, in 1992, respectively.

List of Figures xiii

List of Tables xxv

About the Editors xxvii

Preface xxix

PART I INTRODUCTION

1 Introduction to Cognitive Communications 3
David Grace

1.1 Introduction 3

1.2 A NewWay of Thinking 4

1.3 History of Cognitive Communications 6

1.4 Key Components of Cognitive Communications 8

1.5 Overview of the Rest of the Book 9

1.5.1 Part 2: Wireless Communications 10

1.5.2 Part 3: Application of Distributed Artificial Intelligence 11

1.5.3 Part 4: Regulatory Policy and Economics 12

1.5.4 Part 5: Implementation 13

1.6 Summary and Conclusion 14

References 14

PART II WIRELESS COMMUNICATIONS

2 Cognitive Radio and Networks for Heterogeneous Networking 19
Haesik Kim and Aarne M€ammel€a

2.1 Introduction 19

2.1.1 Historical Sketch 19

2.1.2 Cognitive Radio and Networks 21

2.1.3 Heterogeneous Networks 22

2.2 Cognitive Radio for Heterogeneous Networks 26

2.2.1 Channel Sensing and Network Sensing 26

2.2.2 Interference Mitigation 27

2.2.3 Power Control 31

2.3 Applying Cognitive Networks to Heterogeneous Networks 37

2.3.1 Network Policy for Coexistence of Different Networks 37

2.3.2 Cooperation Mechanisms 39

2.3.3 Network Resource Allocation 41

2.3.4 Self-Organization Mechanisms 44

2.3.5 Handover Mechanisms 45

2.4 Performance Evaluation 47

2.5 Conclusion 50

References 50

3 Channel Assignment and Power Allocation Algorithms in Multi-Carrier-Based Cognitive Radio Environments 53
Musbah Shaat and Faouzi Bader

3.1 Introduction 53

3.2 The Orthogonal Frequency-Division Multiplexing (OFDM) Transmission Scheme 54

3.2.1 Why OFDM is Appropriate for CR 55

3.3 Resource Management in Non-Cognitive OFDM Environments 56

3.3.1 Single User OFDM Systems 56

3.3.2 Multiple User OFDM Systems (OFDMA) 57

3.3.3 Resource Allocation Algorithms in Non-Cognitive OFDM Systems 58

3.4 Resource Management in OFDM-Based Cognitive Radio Systems 58

3.4.1 Algorithms Dealing with In-Band Interference 59

3.4.2 Algorithms Dealing with Mutual Interference 60

3.4.3 System Model 61

3.4.4 Problem Formulation 63

3.4.5 Resource Management in Downlink OFDM-Based CR Systems 64

3.4.6 Resource Management in Uplink OFDM-Based CR Systems 76

3.5 Conclusions 88

References 89

4 Filter Bank Techniques for Multi-Carrier Cognitive Radio Systems 93
Yun Cui, Zhifeng Zhao, Rongpeng Li, Guangchao Zhang and Honggang Zhang

4.1 Introduction 93

4.2 Basic Features of Filter Banks-Based Multi-Carrier Techniques 94

4.2.1 Introduction to the Filter Bank System 95

4.2.2 The Polyphase Structure of Filter Banks 96

4.2.3 Basic Structure of Filter Banks-Based Multi-Carrier Systems 97

4.3 Adaptive Threshold Enhanced Filter Bank for Spectrum Detection in IEEE 802.22 98

4.3.1 Multi-Stage Analysis Filter Banks for Spectrum Detection 99

4.3.2 Complexity and Detection Precision Analysis 101

4.3.3 Spectrum Detection in IEEE 802.22 103

4.3.4 Power Estimation with Adaptive Threshold 106

4.4 Transform Decomposition for Spectrum Interleaving in Multi-Carrier Cognitive Radio Systems 108

4.4.1 FFT Pruning in Cognitive Radio Systems 108

4.4.2 Transform Decomposition for General DFT 110

4.4.3 Improved Transform Decomposition Method for DFT with Sparse Input Points 111

4.4.4 Numerical Results and Computational Complexity Analysis 114

4.5 Remaining Problems in Filter Banks-Based Multi-Carrier Systems 115

4.6 Summary and Conclusion 117

References 117

5 Distributed Clustering of Cognitive Radio Networks: A Message-Passing Approach 119
Kareem E. Baddour, Oktay Ureten and Tricia J. Willink

5.1 Introduction 119

5.1.1 Inter-Node Collaboration in Decentralized Cognitive Networks 119

5.1.2 Scalability Issues and Overhead Costs 120

5.1.3 Self-Organization Based on Distributed Clustering 120

5.2 Clustering Techniques for Cognitive Radio Networks 122

5.3 A Message-Passing Clustering Approach Based on Affinity Propagation 124

5.4 Case Studies 126

5.4.1 Clustering Based on Local Spectrum Availability 127

5.4.2 Sensor Selection for Cooperative Spectrum Sensing 132

5.5 Implementation Challenges 138

5.6 Conclusions 140

References 140

PART III APPLICATION OF DISTRIBUTED ARTIFICIAL INTELLIGENCE

6 Machine Learning Applied to Cognitive Communications 145
Aimilia Bantouna, Kostas Tsagkaris, Vera Stavroulaki, Panagiotis Demestichas and Giorgos Poulios

6.1 Introduction 145

6.2 State of the Art 146

6.3 Learning Techniques 148

6.3.1 Bayesian Statistics 148

6.3.2 Supervised Neural Networks (NNs) 150

6.3.3 Self-Organizing Maps (SOMs): An Unsupervised Neural Network 153

6.3.4 Reinforcement Learning 157

6.4 Advantages and Disadvantages of Applying Machine Learning to Cognitive Radio Networks 158

6.5 Conclusions 159

Acknowledgement 160

References 160

7 Reinforcement Learning for Distributed Power Control and Channel Access in Cognitive Wireless Mesh Networks 163
Xianfu Chen, Zhifeng Zhao and Honggang Zhang

7.1 Introduction 163

7.2 Applying Reinforcement Learning to Distributed Power Control and Channel Access 165

7.2.1 Conjecture-Based Multi-Agent Q-Learning for Distributed Power Control in CogMesh 165

7.2.2 Learning with Dynamic Conjectures for Opportunistic Spectrum Access in CogMesh 176

7.3 Future Challenges 191

7.4 Conclusions 192

References 192

8 Reinforcement Learning-Based Cognitive Radio for Open Spectrum Access 195
Tao Jiang and David Grace

8.1 Open Spectrum Access 195

8.2 Reinforcement Learning-Based Spectrum Sharing in Open Spectrum Bands 196

8.2.1 Learning Model 196

8.2.2 Basic Algorithms 200

8.2.3 Performance 200

8.3 Exploration Control and Efficient Exploration for Reinforcement Learning-Based Cognitive Radio 208

8.3.1 Exploration Control Techniques for Cognitive Radios 208

8.3.2 Efficient Exploration Techniques and Learning Efficiency for Cognitive Radios 218

8.4 Conclusion 229

References 230

9 Learning Techniques for Context Diagnosis and Prediction in Cognitive Communications 231
Aimilia Bantouna, Kostas Tsagkaris, Vera Stavroulaki, Giorgos Poulios and Panagiotis Demestichas

9.1 Introduction 231

9.2 Prediction 232

9.2.1 Building Knowledge: Learning Network Capabilities and User Preferences/ Behaviours 232

9.2.2 Application to Context Diagnosis and Prediction: The Case of Congestion 248

9.3 Future Problems 253

9.4 Conclusions 254

References 255

10 Social Behaviour in Cognitive Radio 257
Husheng Li

10.1 Introduction 257

10.2 Social Behaviour in Cognitive Radio 258

10.2.1 Cooperation Formation 258

10.2.2 Channel Recommendations 261

10.3 Social Network Analysis 267

10.3.1 Model of Recommendation Mechanism 267

10.3.2 Interacting Particles 268

10.3.3 Epidemic Propagation 273

10.4 Conclusions 281

References 281

PART IV REGULATORY POLICY AND ECONOMICS

11 Regulatory Policy and Economics of Cognitive Radio for Secondary Spectrum Access 285
Maziar Nekovee and Peter Anker

11.1 Introduction 285

11.2 Spectrum Regulations: Why and How? 286

11.3 Overview of Regulatory Bodies and Their Inter-Relation 287

11.3.1 ITU 287

11.3.2 CEPT/ECC 288

11.3.3 European Union 289

11.3.4 ETSI 290

11.3.5 National Spectrum Management Authority 291

11.4 Why Secondary Spectrum Access? 291

11.5 Candidate Bands for Secondary Access 293

11.5.1 Terrestrial Broadcasting Bands 294

11.5.2 Radar Bands 294

11.5.3 IMT Bands 295

11.5.4 Military Bands 296

11.6 Regulatory and Policy Issues 296

11.6.1 UK Regulatory Environment 300

11.6.2 US Regulatory Environment 301

11.6.3 European Regulatory Environment 302

11.6.4 Regulatory Environments Elsewhere 303

11.7 Technology Enablers and Options for Secondary Sharing 304

11.7.1 Cognitive Radio 304

11.7.2 Technology Options for Secondary Access 306

11.8 Economic Impact and Business Opportunities of SSA 308

11.8.1 Stakeholders and Economic of SSA 309

11.8.2 Use Cases and Business Models 310

11.9 Outlook 313

11.10 Conclusions 314

Acknowledgements 315

References 315

PART V IMPLEMENTATION

12 Cognitive Radio Networks in TV White Spaces 321
Maziar Nekovee and Dave Wisely

12.1 Introduction 321

12.2 Research and Development Challenges 324

12.2.1 Geolocation Databases 324

12.2.2 Sensing 327

12.2.3 Beacons 330

12.2.4 Physical Layer 330

12.2.5 System Issues 331

12.2.6 Devices 335

12.3 Regulation and Standardization 335

12.3.1 Regulation 335

12.3.2 Standardization 338

12.4 Quantifying Spectrum Opportunities 343

12.5 Commercial Use Cases 346

12.6 Conclusions 354

Acknowledgement 355

References 355

13 Cognitive Femtocell Networks 359
Faisal Tariq and Laurence S. Dooley

13.1 Introduction 359

13.2 Femtocell Network Architecture 361

13.2.1 Underlay and Overlay Architectures for Femtocell Networks 362

13.2.2 Home Femtocell and Enterprise Femtocell 366

13.2.3 Access Mechanism: Closed, Open and Hybrid Access 369

13.2.4 Possible Operating Spectrum 371

13.3 Interference Management Strategies 372

13.3.1 Cross-Tier Interference Management 373

13.3.2 Intra-Tier Interference Management 376

13.4 Self Organized Femtocell Networks (SOFN) 381

13.4.1 Self-Configuration 383

13.4.2 Self-Optimization 383

13.4.3 Self-Healing and Self-Protection 388

13.5 Future Research Directions 388

13.5.1 Green Femtocell Networks 388

13.5.2 Communication Hub for Smart Homes 389

13.5.3 MIMO-Based Interference Alignment for Femtocell Networks 389

13.5.4 Enhanced FFR 390

13.5.5 CoMP-Based Femtocell Network 391

13.5.6 Holistic Approach to SOFN 391

13.6 Conclusion 391

References 391

14 Cognitive Acoustics: A Way to Extend the Lifetime of Underwater Acoustic Sensor Networks 395
Lu Jin, Defeng (David) Huang, Lin Zou and Angela Ying Jun Zhang

14.1 The Concept of Cognitive Acoustics 395

14.2 Underwater Acoustic Communication Channel 397

14.2.1 Propagation Delay 397

14.2.2 Severe Attenuation 397

14.2.3 Ambient Noise 398

14.3 Some Distinct Features of Cognitive Acoustics 401

14.3.1 Purposes of Deployment 401

14.3.2 Grey Space 402

14.3.3 Cost of Field Measurement and System Deployment 402

14.4 Fundamentals of Reinforcement Learning 402

14.4.1 Markov Decision Process 402

14.4.2 Reinforcement Learning 403

14.4.3 Q-Learning 403

14.5 An Application Scenario: Underwater Acoustic Sensor Networks 404

14.5.1 System Description 404

14.5.2 State Space, Action Set and Transition Probabilities 406

14.5.3 Reward Function 407

14.5.4 Routing Protocol Discussion 409

14.6 Numerical Results 410

14.7 Conclusion 414

Acknowledgements 414

References 414

15 CMOS RF Transceiver Considerations for DSA 417
Mark S. Oude Alink, Eric A.M. Klumperink, Andre B.J. Kokkeler, Gerard J.M. Smit and Bram Nauta

15.1 Introduction 417

15.1.1 Terminology 418

15.1.2 Transceivers for DSA: More than an ADC and DAC 420

15.1.3 Flexible Software-Defined Transceiver 421

15.1.4 Why CMOS Transceivers? 421

15.2 DSATransceiver Requirements 421

15.3 Mathematical Abstraction 423

15.4 Filters 426

15.4.1 Integrated Filters 426

15.4.2 External Filters 427

15.5 Receiver Considerations and Implementation 428

15.5.1 Sub-Sampling Receiver 429

15.5.2 Heterodyne Receivers 430

15.5.3 Direct-Conversion Receivers 432

15.6 Cognitive Radio Receivers 436

15.6.1 Wideband RF-Section 436

15.6.2 No External RF-Filterbank 437

15.6.3 Wideband Frequency Generation 447

15.7 Transmitter Considerations and Implementation 449

15.8 Cognitive Radio Transmitters 451

15.8.1 Improving Transmitter Linearity 451

15.8.2 Reducing Harmonic Components 452

15.8.3 The Polyphase Multipath Technique 453

15.9 Spectrum Sensing 456

15.9.1 Analogue Windowing 458

15.9.2 Channelized Receiver 459

15.9.3 Crosscorrelation Spectrum Sensing 459

15.9.4 Improved Image and Harmonic Rejection Using Crosscorrelation 461

15.10 Summary and Conclusions 462

References 462

Index 465