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Cognitive radio communications and networking : principles and practice / / Robert C. Qiu ... [et al.]
| Cognitive radio communications and networking : principles and practice / / Robert C. Qiu ... [et al.] |
| Autore | Qiu Robert Caiming <1966-> |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Chichester, West Sussex, UK : , : John Wiley & Sons Inc., , 2012 |
| Descrizione fisica | 1 online resource (536 p.) |
| Disciplina | 621.382/1 |
| Altri autori (Persone) | QiuRobert C. <1966-> |
| Soggetto topico | Cognitive radio networks |
| ISBN |
1-283-99373-2
1-118-37629-3 1-118-37628-5 1-118-37627-7 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
-- 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.
3.1 Formalism of Quantum Information 51 -- 3.2 Hypothesis Detection for Collaborative Sensing 51 -- 3.3 Sample Covariance Matrix 55 -- 3.3.1 The Data Matrix 56 -- 3.4 Random Matrices with Independent Rows 63 -- 3.5 The Multivariate Normal Distribution 67 -- 3.6 Sample Covariance Matrix Estimation and Matrix Compressed Sensing 77 -- 3.6.1 The Maximum Likelihood Estimation 81 -- 3.6.2 Likelihood Ratio Test (Wilks Test) for Multisample Hypotheses 83 -- 3.7 Likelihood Ratio Test 84 -- 3.7.1 General Gaussian Detection and Estimator-Correlator Structure 84 -- 3.7.2 Tests with Repeated Observations 90 -- 3.7.3 Detection Using Sample Covariance Matrices 94 -- 3.7.4 GLRT for Multiple Random Vectors 95 -- 3.7.5 Linear Discrimination Functions 97 -- 3.7.6 Detection of Correlated Structure for Complex Random Vectors 98 -- 4 Hypothesis Detection of Noncommutative Random Matrices 101 -- 4.1 Why Noncommutative Random Matrices? 101 -- 4.2 Partial Orders of Covariance Matrices: A < B 102 -- 4.3 Partial Ordering of Completely Positive Mappings: (A) < (B) 104 -- 4.4 Partial Ordering of Matrices Using Majorization: A ≺ B 105 -- 4.5 Partial Ordering of Unitarily Invariant Norms: |||A||| < |||B||| 109 -- 4.6 Partial Ordering of Positive Definite Matrices of Many Copies: K k=1 Ak ≤ K k=1 Bk 109 -- 4.7 Partial Ordering of Positive Operator Valued Random Variables: Prob(A ≤ X ≤ B) 110 -- 4.8 Partial Ordering Using Stochastic Order: A ≤st B 115 -- 4.9 Quantum Hypothesis Detection 115 -- 4.10 Quantum Hypothesis Testing for Many Copies 118 -- 5 Large Random Matrices 119 -- 5.1 Large Dimensional Random Matrices: Moment Approach, Stieltjes Transform and Free Probability 119 -- 5.2 Spectrum Sensing Using Large Random Matrices 121 -- 5.2.1 System Model 121 -- 5.2.2 Marchenko-Pastur Law 124 -- 5.3 Moment Approach 129 -- 5.3.1 Limiting Spectral Distribution 130 -- 5.3.2 Limits of Extreme Eigenvalues 133 -- 5.3.3 Convergence Rates of Spectral Distributions 136 -- 5.3.4 Standard Vector-In, Vector-Out Model 137. 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. |
| Record Nr. | UNINA-9910137615003321 |
Qiu Robert Caiming <1966->
|
||
| Chichester, West Sussex, UK : , : John Wiley & Sons Inc., , 2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Cognitive radio communications and networking : principles and practice / / Robert C. Qiu ... [et al.]
| Cognitive radio communications and networking : principles and practice / / Robert C. Qiu ... [et al.] |
| Autore | Qiu Robert Caiming <1966-> |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Chichester, West Sussex, UK : , : John Wiley & Sons Inc., , 2012 |
| Descrizione fisica | 1 online resource (536 p.) |
| Disciplina | 621.382/1 |
| Altri autori (Persone) | QiuRobert C. <1966-> |
| Soggetto topico | Cognitive radio networks |
| ISBN |
1-283-99373-2
1-118-37629-3 1-118-37628-5 1-118-37627-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
-- 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.
3.1 Formalism of Quantum Information 51 -- 3.2 Hypothesis Detection for Collaborative Sensing 51 -- 3.3 Sample Covariance Matrix 55 -- 3.3.1 The Data Matrix 56 -- 3.4 Random Matrices with Independent Rows 63 -- 3.5 The Multivariate Normal Distribution 67 -- 3.6 Sample Covariance Matrix Estimation and Matrix Compressed Sensing 77 -- 3.6.1 The Maximum Likelihood Estimation 81 -- 3.6.2 Likelihood Ratio Test (Wilks Test) for Multisample Hypotheses 83 -- 3.7 Likelihood Ratio Test 84 -- 3.7.1 General Gaussian Detection and Estimator-Correlator Structure 84 -- 3.7.2 Tests with Repeated Observations 90 -- 3.7.3 Detection Using Sample Covariance Matrices 94 -- 3.7.4 GLRT for Multiple Random Vectors 95 -- 3.7.5 Linear Discrimination Functions 97 -- 3.7.6 Detection of Correlated Structure for Complex Random Vectors 98 -- 4 Hypothesis Detection of Noncommutative Random Matrices 101 -- 4.1 Why Noncommutative Random Matrices? 101 -- 4.2 Partial Orders of Covariance Matrices: A < B 102 -- 4.3 Partial Ordering of Completely Positive Mappings: (A) < (B) 104 -- 4.4 Partial Ordering of Matrices Using Majorization: A ≺ B 105 -- 4.5 Partial Ordering of Unitarily Invariant Norms: |||A||| < |||B||| 109 -- 4.6 Partial Ordering of Positive Definite Matrices of Many Copies: K k=1 Ak ≤ K k=1 Bk 109 -- 4.7 Partial Ordering of Positive Operator Valued Random Variables: Prob(A ≤ X ≤ B) 110 -- 4.8 Partial Ordering Using Stochastic Order: A ≤st B 115 -- 4.9 Quantum Hypothesis Detection 115 -- 4.10 Quantum Hypothesis Testing for Many Copies 118 -- 5 Large Random Matrices 119 -- 5.1 Large Dimensional Random Matrices: Moment Approach, Stieltjes Transform and Free Probability 119 -- 5.2 Spectrum Sensing Using Large Random Matrices 121 -- 5.2.1 System Model 121 -- 5.2.2 Marchenko-Pastur Law 124 -- 5.3 Moment Approach 129 -- 5.3.1 Limiting Spectral Distribution 130 -- 5.3.2 Limits of Extreme Eigenvalues 133 -- 5.3.3 Convergence Rates of Spectral Distributions 136 -- 5.3.4 Standard Vector-In, Vector-Out Model 137. 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. |
| Record Nr. | UNINA-9910807424303321 |
|
Qiu Robert Caiming <1966->
|
||
| Chichester, West Sussex, UK : , : John Wiley & Sons Inc., , 2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||