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Cognitive communications : distributed artificial intelligence (DAI), regulatory policy & economics, implementation / / editors David Grace, Honggang Zhang
Cognitive communications : distributed artificial intelligence (DAI), regulatory policy & economics, implementation / / editors David Grace, Honggang Zhang
Pubbl/distr/stampa Chichester, West Sussex : , : Wiley, , 2012
Descrizione fisica 1 online resource (501 p.)
Disciplina 621.384
Altri autori (Persone) GraceDavid <1970->
ZhangHonggang <1967->
Soggetto topico Cognitive radio networks
Distributed artificial intelligence
Telecommunication policy
ISBN 1-118-36033-8
1-299-31471-6
1-118-36032-X
1-118-36031-1
Classificazione TEC041000
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto -- 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 New Way 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 MÈammelÈa -- 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.
Record Nr. UNINA-9910130597203321
Chichester, West Sussex : , : Wiley, , 2012
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Cognitive communications : distributed artificial intelligence (DAI), regulatory policy & economics, implementation / / editors David Grace, Honggang Zhang
Cognitive communications : distributed artificial intelligence (DAI), regulatory policy & economics, implementation / / editors David Grace, Honggang Zhang
Pubbl/distr/stampa Chichester, West Sussex : , : Wiley, , 2012
Descrizione fisica 1 online resource (501 p.)
Disciplina 621.384
Altri autori (Persone) GraceDavid <1970->
ZhangHonggang <1967->
Soggetto topico Cognitive radio networks
Distributed artificial intelligence
Telecommunication policy
ISBN 1-118-36033-8
1-299-31471-6
1-118-36032-X
1-118-36031-1
Classificazione TEC041000
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto -- 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 New Way 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 MÈammelÈa -- 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.
Record Nr. UNINA-9910821647903321
Chichester, West Sussex : , : Wiley, , 2012
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui