This book discusses in-depth the concept of distributed artificial intelligence (DAI) and its application to cognitive communications In this book, the authors present an overview of cognitive communications, encompassing both cognitive radio and cognitive networks, and also other application areas such as cognitive acoustics. The book also explains the specific rationale for the integration of different forms of distributed artificial intelligence into cognitive communications, something which is often neglected in many forms of technical contributions available today. Furthermore, the…mehr
This book discusses in-depth the concept of distributed artificial intelligence (DAI) and its application to cognitive communications
In this book, the authors present an overview of cognitive communications, encompassing both cognitive radio and cognitive networks, and also other application areas such as cognitive acoustics. The book also explains the specific rationale for the integration of different forms of distributed artificial intelligence into cognitive communications, something which is often neglected in many forms of technical contributions available today. Furthermore, the chapters are divided into four disciplines: wireless communications, distributed artificial intelligence, regulatory policy and economics and implementation. The book contains contributions from leading experts (academia and industry) in the field.
Key Features: Covers the broader field of cognitive communications as a whole, addressing application to communication systems in general (e.g. cognitive acoustics and Distributed Artificial Intelligence (DAI) Illustrates how different DAI based techniques can be used to self-organise the radio spectrum Explores the regulatory, policy and economic issues of cognitive communications in the context of secondary spectrum access Discusses application and implementation of cognitive communications techniques in different application areas (e.g. Cognitive Femtocell Networks (CFN) Written by experts in the field from both academia and industry
Cognitive Communications will be an invaluable guide for research community (PhD students, researchers) in the areas of wireless communications, and development engineers involved in the design and development of mobile, portable and fixed wireless systems., wireless network design engineer. Undergraduate and postgraduate students on elective courses in electronic engineering or computer science, and the research and engineering community will also find this book of interest.
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.
Inhaltsangabe
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 1.6 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 2.2 Cognitive Radio for Heterogeneous Networks 26 2.3 Applying Cognitive Networks to Heterogeneous Networks 37 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 3.3 Resource Management in Non-Cognitive OFDM Environments 56 3.4 Resource Management in OFDM-Based Cognitive Radio Systems 58 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 4.3 Adaptive Threshold Enhanced Filter Bank for Spectrum Detection in IEEE 802.22 98 4.4 Transform Decomposition for Spectrum Interleaving in Multi-Carrier Cognitive Radio Systems 108 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 5.2 Clustering Techniques for Cognitive Radio Networks 122 5.3 A Message-Passing Clustering Approach Based on Affinity Propagation 124 5.4 Case Studies 126 5.5 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 6.4 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 7.3 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 8.3 Exploration Control and Efficient Exploration for Reinforcement Learning-Based Cognitive Radio 208 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 9.3 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 10.3 Social Network Analysis 267 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 11.4 Why Secondary Spectrum Access? 291 11.5 Candidate Bands for Secondary Access 293 11.6 Regulatory and Policy Issues 296 11.7 Technology Enablers and Options for Secondary Sharing 304 11.8 Economic Impact and Business Opportunities of SSA 308 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 12.3 Regulation and Standardization 335 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 13.3 Interference Management Strategies 372 13.4 Self Organized Femtocell Networks (SOFN) 381 13.5 Future Research Directions 388 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 14.3 Some Distinct Features of Cognitive Acoustics 401 14.4 Fundamentals of Reinforcement Learning 402 14.5 An Application Scenario: Underwater Acoustic Sensor Networks 404 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 15.2 DSATransceiver Requirements 421 15.3 Mathematical Abstraction 423 15.4 Filters 426 15.5 Receiver Considerations and Implementation 428 15.6 Cognitive Radio Receivers 436 15.7 Transmitter Considerations and Implementation 449 15.8 Cognitive Radio Transmitters 451 15.9 Spectrum Sensing 456 15.10 Summary and Conclusions 462 References 462 Index 465
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 1.6 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 2.2 Cognitive Radio for Heterogeneous Networks 26 2.3 Applying Cognitive Networks to Heterogeneous Networks 37 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 3.3 Resource Management in Non-Cognitive OFDM Environments 56 3.4 Resource Management in OFDM-Based Cognitive Radio Systems 58 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 4.3 Adaptive Threshold Enhanced Filter Bank for Spectrum Detection in IEEE 802.22 98 4.4 Transform Decomposition for Spectrum Interleaving in Multi-Carrier Cognitive Radio Systems 108 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 5.2 Clustering Techniques for Cognitive Radio Networks 122 5.3 A Message-Passing Clustering Approach Based on Affinity Propagation 124 5.4 Case Studies 126 5.5 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 6.4 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 7.3 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 8.3 Exploration Control and Efficient Exploration for Reinforcement Learning-Based Cognitive Radio 208 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 9.3 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 10.3 Social Network Analysis 267 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 11.4 Why Secondary Spectrum Access? 291 11.5 Candidate Bands for Secondary Access 293 11.6 Regulatory and Policy Issues 296 11.7 Technology Enablers and Options for Secondary Sharing 304 11.8 Economic Impact and Business Opportunities of SSA 308 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 12.3 Regulation and Standardization 335 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 13.3 Interference Management Strategies 372 13.4 Self Organized Femtocell Networks (SOFN) 381 13.5 Future Research Directions 388 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 14.3 Some Distinct Features of Cognitive Acoustics 401 14.4 Fundamentals of Reinforcement Learning 402 14.5 An Application Scenario: Underwater Acoustic Sensor Networks 404 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 15.2 DSATransceiver Requirements 421 15.3 Mathematical Abstraction 423 15.4 Filters 426 15.5 Receiver Considerations and Implementation 428 15.6 Cognitive Radio Receivers 436 15.7 Transmitter Considerations and Implementation 449 15.8 Cognitive Radio Transmitters 451 15.9 Spectrum Sensing 456 15.10 Summary and Conclusions 462 References 462 Index 465
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