Cognitive Communications
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Persons
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.
Content
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 MEURammelEURa
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
