Cognitive radio communication and networking : principles and practice /

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A comprehensive examination of the basic mathematical tools, progressing to more advanced concepts and discussions about the future of cognitive radio Cognitive radio is a paradigm for wireless communication in which either a network or a wireless node changes its transmission or reception parameter...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Qiu, Robert C., 1966-
Otros Autores: Hu, Zhen, 1981-, Li, Husheng, 1975-
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Chichester, West Sussex, UK : Wiley-Blackwell, 2012.
Temas:
Cognitive radio networks.
Radio cognitive.
Cognitive radio networks
Acceso en línea:Texto completo
Texto completo
Tabla de Contenidos:
  • -- Preface xv
  • 1 Introduction 1
  • 1.1 Vision: "Big Data" 1
  • 1.2 Cognitive Radio: System Concepts 2
  • 1.3 Spectrum Sensing Interface and Data Structures 2
  • 1.4 Mathematical Machinery 4
  • 1.4.1 Convex Optimization 4
  • 1.4.2 Game Theory 6
  • 1.4.3 "Big Data" Modeled as Large Random Matrices 6
  • 1.5 Sample Covariance Matrix 10
  • 1.6 Large Sample Covariance Matrices of Spiked Population Models 11
  • 1.7 Random Matrices and Noncommutative Random Variables 12
  • 1.8 Principal Component Analysis 13
  • 1.9 Generalized Likelihood Ratio Test (GLRT) 13
  • 1.10 Bregman Divergence for Matrix Nearness 13
  • 2 Spectrum Sensing: Basic Techniques 15
  • 2.1 Challenges 15
  • 2.2 Energy Detection: No Prior Information about Deterministic or Stochastic Signal 15
  • 2.2.1 Detection in White Noise: Lowpass Case 16
  • 2.2.2 Time-Domain Representation of the Decision Statistic 19
  • 2.2.3 Spectral Representation of the Decision Statistic 19
  • 2.2.4 Detection and False Alarm Probabilities over AWGN Channels 20
  • 2.2.5 Expansion of Random Process in Orthonormal Series with Uncorrelated Coefficients: The Karhunen-Loeve Expansion 21
  • 2.3 Spectrum Sensing Exploiting Second-Order Statistics 23
  • 2.3.1 Signal Detection Formulation 23
  • 2.3.2 Wide-Sense Stationary Stochastic Process: Continuous-Time 24
  • 2.3.3 Nonstationary Stochastic Process: Continuous-Time 25
  • 2.3.4 Spectrum Correlation-Based Spectrum Sensing for WSS Stochastic Signal: Heuristic Approach 29
  • 2.3.5 Likelihood Ratio Test of Discrete-Time WSS Stochastic Signal 32
  • 2.3.6 Asymptotic Equivalence between Spectrum Correlation and Likelihood Ratio Test 35
  • 2.3.7 Likelihood Ratio Test of Continuous-Time Stochastic Signals in Noise: Selin's Approach 36
  • 2.4 Statistical Pattern Recognition: Exploiting Prior Information about Signal through Machine Learning 39
  • 2.4.1 Karhunen-Loeve Decomposition for Continuous-Time Stochastic Signal 39
  • 2.5 Feature Template Matching 42
  • 2.6 Cyclostationary Detection 47
  • 3 Classical Detection 51.
  • 5.3.5 Generalized Densities 138
  • 5.4 Stieltjes Transform 139
  • 5.4.1 Basic Theorems 143
  • 5.4.2 Large Random Hankel, Markov and Toepltiz Matrices 149
  • 5.4.3 Information Plus Noise Model of Random Matrices 152
  • 5.4.4 Generalized Likelihood Ratio Test Using Large Random Matrices 157
  • 5.4.5 Detection of High-Dimensional Signals in White Noise 164
  • 5.4.6 Eigenvalues of (A + B)-1B and Applications 169
  • 5.4.7 Canonical Correlation Analysis 171
  • 5.4.8 Angles and Distances between Subspaces 173
  • 5.4.9 Multivariate Linear Model 173
  • 5.4.10 Equality of Covariance Matrices 174
  • 5.4.11 Multiple Discriminant Analysis 174
  • 5.5 Case Studies and Applications 175
  • 5.5.1 Fundamental Example of Using Large Random Matrix 175
  • 5.5.2 Stieltjes Transform 177
  • 5.5.3 Free Deconvolution 178
  • 5.5.4 Optimal Precoding of MIMO Systems 178
  • 5.5.5 Marchenko and Pastur's Probability Distribution 179
  • 5.5.6 Convergence and Fluctuations Extreme Eigenvalues 180
  • 5.5.7 Information plus Noise Model and Spiked Models 180
  • 5.5.8 Hypothesis Testing and Spectrum Sensing 183
  • 5.5.9 Energy Estimation in a Wireless Network 185
  • 5.5.10 Multisource Power Inference 187
  • 5.5.11 Target Detection, Localization, and Reconstruction 187
  • 5.5.12 State Estimation and Malignant Attacker in the Smart Grid 191
  • 5.5.13 Covariance Matrix Estimation 193
  • 5.5.14 Deterministic Equivalents 197
  • 5.5.15 Local Failure Detection and Diagnosis 200
  • 5.6 Regularized Estimation of Large Covariance Matrices 200
  • 5.6.1 Regularized Covariance Estimates 201
  • 5.6.2 Banding the Inverse 203
  • 5.6.3 Covariance Regularization by Thresholding 204
  • 5.6.4 Regularized Sample Covariance Matrices 206
  • 5.6.5 Optimal Rates of Convergence for Covariance Matrix Estimation 208
  • 5.6.6 Banding Sample Autocovariance Matrices of Stationary Processes 211
  • 5.7 Free Probability 213
  • 5.7.1 Large Random Matrices and Free Convolution 218
  • 5.7.2 Vandermonde Matrices 221
  • 5.7.3 Convolution and Deconvolution with Vandermonde Matrices 229.
  • 5.7.4 Finite Dimensional Statistical Inference 232
  • 6 Convex Optimization 235
  • 6.1 Linear Programming 237
  • 6.2 Quadratic Programming 238
  • 6.3 Semidefinite Programming 239
  • 6.4 Geometric Programming 239
  • 6.5 Lagrange Duality 241
  • 6.6 Optimization Algorithm 242
  • 6.6.1 Interior Point Methods 242
  • 6.6.2 Stochastic Methods 243
  • 6.7 Robust Optimization 244
  • 6.8 Multiobjective Optimization 248
  • 6.9 Optimization for Radio Resource Management 249
  • 6.10 Examples and Applications 250
  • 6.10.1 Spectral Efficiency for Multiple Input Multiple Output Ultra-Wideband Communication System 250
  • 6.10.2 Wideband Waveform Design for Single Input Single Output Communication System with Noncoherent Receiver 256
  • 6.10.3 Wideband Waveform Design for Multiple Input Single Output Cognitive Radio 262
  • 6.10.4 Wideband Beamforming Design 268
  • 6.10.5 Layering as Optimization Decomposition for Cognitive Radio Network 272
  • 6.11 Summary 282
  • 7 Machine Learning 283
  • 7.1 Unsupervised Learning 288
  • 7.1.1 Centroid-Based Clustering 288
  • 7.1.2 k-Nearest Neighbors 289
  • 7.1.3 Principal Component Analysis 289
  • 7.1.4 Independent Component Analysis 290
  • 7.1.5 Nonnegative Matrix Factorization 291
  • 7.1.6 Self-Organizing Map 292
  • 7.2 Supervised Learning 293
  • 7.2.1 Linear Regression 293
  • 7.2.2 Logistic Regression 294
  • 7.2.3 Artificial Neural Network 294
  • 7.2.4 Decision Tree Learning 294
  • 7.2.5 Naive Bayes Classifier 295
  • 7.2.6 Support Vector Machines 295
  • 7.3 Semisupervised Learning 298
  • 7.3.1 Constrained Clustering 298
  • 7.3.2 Co-Training 298
  • 7.3.3 Graph-Based Methods 299
  • 7.4 Transductive Inference 299
  • 7.5 Transfer Learning 299
  • 7.6 Active Learning 299
  • 7.7 Reinforcement Learning 300
  • 7.7.1 Q-Learning 300
  • 7.7.2 Markov Decision Process 301
  • 7.7.3 Partially Observable MDPs 302
  • 7.8 Kernel-Based Learning 303
  • 7.9 Dimensionality Reduction 304
  • 7.9.1 Kernel Principal Component Analysis 305
  • 7.9.2 Multidimensional Scaling 307.
  • 7.9.3 Isomap 308
  • 7.9.4 Locally-Linear Embedding 308
  • 7.9.5 Laplacian Eigenmaps 309
  • 7.9.6 Semidefinite Embedding 309
  • 7.10 Ensemble Learning 311
  • 7.11 Markov Chain Monte Carlo 312
  • 7.12 Filtering Technique 313
  • 7.12.1 Kalman Filtering 314
  • 7.12.2 Particle Filtering 318
  • 7.12.3 Collaborative Filtering 319
  • 7.13 Bayesian Network 320
  • 7.14 Summary 321
  • 8 Agile Transmission Techniques (I): Multiple Input Multiple Output 323
  • 8.1 Benefits of MIMO 323
  • 8.1.1 Array Gain 323
  • 8.1.2 Diversity Gain 323
  • 8.1.3 Multiplexing Gain 324
  • 8.2 Space Time Coding 324
  • 8.2.1 Space Time Block Coding 325
  • 8.2.2 Space Time Trellis Coding 326
  • 8.2.3 Layered Space Time Coding 326
  • 8.3 Multi-User MIMO 327
  • 8.3.1 Space-Division Multiple Access 327
  • 8.3.2 MIMO Broadcast Channel 328
  • 8.3.3 MIMO Multiple Access Channel 330
  • 8.3.4 MIMO Interference Channel 331
  • 8.4 MIMO Network 334
  • 8.5 MIMO Cognitive Radio Network 336
  • 8.6 Summary 337
  • 9 Agile Transmission Techniques (II): Orthogonal Frequency Division Multiplexing 339
  • 9.1 OFDM Implementation 339
  • 9.2 Synchronization 341
  • 9.3 Channel Estimation 343
  • 9.4 Peak Power Problem 345
  • 9.5 Adaptive Transmission 345
  • 9.6 Spectrum Shaping 347
  • 9.7 Orthogonal Frequency Division Multiple Access 347
  • 9.8 MIMO OFDM 349
  • 9.9 OFDM Cognitive Radio Network 349
  • 9.10 Summary 350
  • 10 Game Theory 351
  • 10.1 Basic Concepts of Games 351
  • 10.1.1 Elements of Games 351
  • 10.1.2 Nash Equilibrium: Definition and Existence 352
  • 10.1.3 Nash Equilibrium: Computation 354
  • 10.1.4 Nash Equilibrium: Zero-Sum Games 355
  • 10.1.5 Nash Equilibrium: Bayesian Case 355
  • 10.1.6 Nash Equilibrium: Stochastic Games 356
  • 10.2 Primary User Emulation Attack Games 360
  • 10.2.1 PUE Attack 360
  • 10.2.2 Two-Player Case: A Strategic-Form Game 361
  • 10.2.3 Game in Queuing Dynamics: A Stochastic Game 362
  • 10.3 Games in Channel Synchronization 368
  • 10.3.1 Background of the Game 368
  • 10.3.2 System Model 368.
  • 10.3.3 Game Formulation 369
  • 10.3.4 Bayesian Equilibrium 370
  • 10.3.5 Numerical Results 371
  • 10.4 Games in Collaborative Spectrum Sensing 372
  • 10.4.1 False Report Attack 373
  • 10.4.2 Game Formulation 373
  • 10.4.3 Elements of Game 374
  • 10.4.4 Bayesian Equilibrium 376
  • 10.4.5 Numerical Results 379
  • 11 Cognitive Radio Network 381
  • 11.1 Basic Concepts of Networks 381
  • 11.1.1 Network Architecture 381
  • 11.1.2 Network Layers 382
  • 11.1.3 Cross-Layer Design 384
  • 11.1.4 Main Challenges in Cognitive Radio Networks 384
  • 11.1.5 Complex Networks 385
  • 11.2 Channel Allocation in MAC Layer 386
  • 11.2.1 Problem Formulation 386
  • 11.2.2 Scheduling Algorithm 387
  • 11.2.3 Solution 389
  • 11.2.4 Discussion 390
  • 11.3 Scheduling in MAC Layer 391
  • 11.3.1 Network Model 391
  • 11.3.2 Goal of Scheduling 393
  • 11.3.3 Scheduling Algorithm 393
  • 11.3.4 Performance of the CNC Algorithm 395
  • 11.3.5 Distributed Scheduling Algorithm 396
  • 11.4 Routing in Network Layer 396
  • 11.4.1 Challenges of Routing in Cognitive Radio 397
  • 11.4.2 Stationary Routing 398
  • 11.4.3 Dynamic Routing 402
  • 11.5 Congestion Control in Transport Layer 404
  • 11.5.1 Congestion Control in Internet 404
  • 11.5.2 Challenges in Cognitive Radio 405
  • 11.5.3 TP-CRAHN 406
  • 11.5.4 Early Start Scheme 408
  • 11.6 Complex Networks in Cognitive Radio 417
  • 11.6.1 Brief Introduction to Complex Networks 418
  • 11.6.2 Connectivity of Cognitive Radio Networks 421
  • 11.6.3 Behavior Propagation in Cognitive Radio Networks 423
  • 12 Cognitive Radio Network as Sensors 427
  • 12.1 Intrusion Detection by Machine Learning 429
  • 12.2 Joint Spectrum Sensing and Localization 429
  • 12.3 Distributed Aspect Synthetic Aperture Radar 429
  • 12.4 Wireless Tomography 433
  • 12.5 Mobile Crowdsensing 434
  • 12.6 Integration of 3S 435
  • 12.7 The Cyber-Physical System 435
  • 12.8 Computing 436
  • 12.8.1 Graphics Processor Unit 437
  • 12.8.2 Task Distribution and Load Balancing 437
  • 12.9 Security and Privacy 438.
  • 12.10 Summary 438
  • Appendix A Matrix Analysis 441
  • A.1 Vector Spaces and Hilbert Space 441
  • A.2 Transformations 443
  • A.3 Trace 444
  • A.4 Basics of C ∗-Algebra 444
  • A.5 Noncommunicative Matrix-Valued Random Variables 445
  • A.6 Distances and Projections 447
  • A.6.1 Matrix Inequalities 450
  • A.6.2 Partial Ordering of Positive Semidefinite Matrices 451
  • A.6.3 Partial Ordering of Hermitian Matrices 451
  • References 453
  • Index 511.

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