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Qiu, Robert Caiming

Cognitive Radio Communication and Networking

Principles and Practice

€ 122.95

The author presents a unified treatment of this highly interdisciplinary topic to help define the notion of cognitive radio.


Taal / Language : English

Inhoudsopgave:
Preface 1 Introduction 1.1 Vision: “Big Data” 1.2 Cognitive Radio: System Concepts 1.3 Spectrum Sensing Interface and Data Structures 1.4 Mathematical Machinery 1.4.1 Convex Optimization 1.4.2 Game Theory 1.4.3 “Big Data” Modeled as Large Random Matrices 1.5 Sample Covariance Matrix 1.6 Large Sample Covariance Matrices of Spiked Population Models 1.7 Random Matrices and Noncommutative Random Variables 1.8 Principal Component Analysis 1.9 Generalized Likelihood Ratio Test (GLRT) 1.10 Bregman Divergence for Matrix Nearness 2 Spectrum Sensing: Basic Techniques 2.1 Challenges 2.2 Energy Detection: No Prior Information about Deterministic or Stochastic Signal 2.2.1 Detection in White Noise: Lowpass Case 2.2.2 Time-Domain Representation of the Decision Statistic 2.2.3 Spectral Representation of the Decision Statistic 2.2.4 Detection and False Alarm Probabilities over AWGN Channels 2.2.5 Expansion of Random Process in Orthonormal Series with Uncorrelated Coefficients: The Karhunen-Loeve Expansion 2.3 Spectrum Sensing Exploiting Second-Order Statistics 2.3.1 Signal Detection Formulation 2.3.2 Wide-Sense Stationary Stochastic Process: Continuous-Time 2.3.3 Nonstationary Stochastic Process: Continuous-Time 2.3.4 Spectrum Correlation Based Spectrum Sensing for WSS Stochastic Signal: Heuristic Approach 2.3.5 Likelihood Ratio Test of Discrete-Time WSS Stochastic Signal 2.3.6 Asymptotic Equivalence between Spectrum Correlation and Likelihood Ratio Test 2.3.7 Likelihood Ratio Test of Continuous-Time Stochastic Signals in Noise: Selin’s Approach 2.4 Statistical Pattern Recognition: Exploiting Prior Information about Signal through Machine Learning 2.4.1 Karhunen-Loeve Decomposition for Continuous-Time Stochastic Signal 2.5 Feature Template Matching 2.6 Cyclostationary Detection 3 Classical Detection 3.1 Formalism of Quantum Information 3.2 Hypothesis Detection for Collaborative Sensing 3.3 Sample Covariance Matrix 3.3.1 The Data Matrix 3.4 Random Matrices with Independent Rows 3.5 The Multivariate Normal Distribution 3.6 Sample Covariance Matrix Estimation and Matrix Compressed Sensing 3.6.1 The Maximum Likelihood Estimation 3.6.2 Likelihood Ratio Test (Wilks’ - Test) for Multisample Hypotheses 3.7 Likelihood Ratio Test 3.7.1 General Gaussian Detection and Estimator-Correlator Structure 3.7.2 Tests with Repeated Observations 3.7.3 Detection Using Sample Covariance Matrices 3.7.4 GLRT for Multiple Random Vectors 3.7.5 Linear Discrimination Functions 3.7.6 Detection of Correlated Structure for Complex Random Vectors 4 Hypothesis Detection of Noncommutative Random Matrices 4.1 Why Noncommutative Random Matrices? 4.2 Partial Orders of Covariance Matrices: A < B 1 4.3 Partial Ordering of Completely Positive Mappings: -( A ) < -( B ) 4.4 Partial Ordering of Matrices Using Majorization: A B 4.5 Partial Ordering of Unitarily Invariant Norms: ||| A ||| < ||| B ||| 4.6 Partial Ordering of Positive Definite Matrices of Many Copies:- Kk =1 A k ≤ - Kk =1 B k 4.7 Partial Ordering of Positive Operator Valued Random Variables: Prob (A ≤ X ≤ B) 4.8 Partial Ordering Using Stochastic Order: A ≤ st B 4.9 Quantum Hypothesis Detection 4.10 Quantum Hypothesis Testing for Many Copies 5 Large Random Matrices 5.1 Large Dimensional Random Matrices: Moment Approach, Stieltjes Transform and Free Probability 5.2 Spectrum Sensing Using Large Random Matrices 5.2.1 System Model 5.2.2 Marchenko-Pastur Law 5.3 Moment Approach 5.3.1 Limiting Spectral Distribution 5.3.2 Limits of Extreme Eigenvalues 5.3.3 Convergence Rates of Spectral Distributions 5.3.4 Standard Vector-In, Vector-Out Model 5.3.5 Generalized Densities 5.4 Stieltjes Transform 5.4.1 Basic Theorems 5.4.2 Large Random Hankel, Markov and Toepltiz Matrices 5.4.3 Information Plus Noise Model of Random Matrices 5.4.4 Generalized Likelihood Ratio Test Using Large Random Matrices 5.4.5 Detection of High-Dimensional Signals in White Noise 5.4.6 Eigenvalues of ( A + B ) −1 B and Applications 5.4.7 Canonical Correlation Analysis 5.4.8 Angles and Distances between Subspaces 5.4.9 Multivariate Linear Model 5.4.10 Equality of Covariance Matrices 5.4.11 Multiple Discriminant Analysis 5.5 Case Studies and Applications 5.5.1 Fundamental Example of Using Large Random Matrix 5.5.2 Stieltjes Transform 5.5.3 Free Deconvolution 5.5.4 Optimal Precoding of MIMO Systems 5.5.5 Marchenko and Pastur’s Probability Distribution 5.5.6 Convergence and Fluctuations Extreme Eigenvalues 5.5.7 Information plus Noise Model and Spiked Models 5.5.8 Hypothesis Testing and Spectrum Sensing 5.5.9 Energy Estimation in a Wireless Network 5.5.10 Multisource Power Inference 5.5.11 Target Detection, Localization, and Reconstruction 5.5.12 State Estimation and Malignant Attacker in the Smart Grid 5.5.13 Covariance Matrix Estimation 5.5.14 Deterministic Equivalents 5.5.15 Local Failure Detection and Diagnosis 5.6 Regularized Estimation of Large Covariance Matrices 5.6.1 Regularized Covariance Estimates 5.6.2 Banding the Inverse 5.6.3 Covariance Regularization by Thresholding 5.6.4 Regularized Sample Covariance Matrices 5.6.5 Optimal Rates of Convergence for Covariance Matrix Estimation 5.6.6 Banding Sample Autocovariance Matrices of Stationary Processes 5.7 Free Probability 5.7.1 Large Random Matrices and Free Convolution 5.7.2 Vandermonde Matrices 5.7.3 Convolution and Deconvolution with Vandermonde Matrices 5.7.4 Finite Dimensional Statistical Inference 6 Convex Optimization 6.1 Linear Programming 6.2 Quadratic Programming 6.3 Semidefinite Programming 6.4 Geometric Programming 6.5 Lagrange Duality 6.6 Optimization Algorithm 6.6.1 Interior Point Methods 6.6.2 Stochastic Methods 6.7 Robust Optimization 6.8 Multiobjective Optimization 6.9 Optimization for Radio Resource Management 6.10 Examples and Applications 6.10.1 Spectral Efficiency for Multiple Input Multiple Output Ultra-Wideband Communication System 6.10.2 Wideband Waveform Design for Single Input Single Output Communication System with Noncoherent Receiver 6.10.3 Wideband Waveform Design for Multiple Input Single Output Cognitive Radio 6.10.4 Wideband Beamforming Design 6.10.5 Layering as Optimization Decomposition for Cognitive Radio Network 6.11 Summary 7 Machine Learning 7.1 Unsupervised Learning 7.1.1 Centroid-Based Clustering 7.1.2 k -Nearest Neighbors 7.1.3 Principal Component Analysis 7.1.4 Independent Component Analysis 7.1.5 Nonnegative Matrix Factorization 7.1.6 Self-Organizing Map 7.2 Supervised Learning 7.2.1 Linear Regression 7.2.2 Logistic Regression 7.2.3 Artificial Neural Network 7.2.4 Decision Tree Learning 7.2.5 Naive Bayes Classifier 7.2.6 Support Vector Machines 7.3 Semisupervised Learning 7.3.1 Constrained Clustering 7.3.2 Co-Training 7.3.3 Graph-Based Methods 7.4 Transductive Inference 7.5 Transfer Learning 7.6 Active Learning 7.7 Reinforcement Learning 7.7.1 Q-Learning 7.7.2 Markov Decision Process 7.7.3 Partially Observable MDPs 7.8 Kernel-Based Learning 7.9 Dimensionality Reduction 7.9.1 Kernel Principal Component Analysis 7.9.2 Multidimensional Scaling 7.9.3 Isomap 7.9.4 Locally-Linear Embedding 7.9.5 Laplacian Eigenmaps 7.9.6 Semidefinite Embedding 7.10 Ensemble Learning 7.11 Markov Chain Monte Carlo 7.12 Filtering Technique 7.12.1 Kalman Filtering 7.12.2 Particle Filtering 7.12.3 Collaborative Filtering 7.13 Bayesian Network 7.14 Summary 8 Agile Transmission Techniques (I): Multiple Input Multiple Output 8.1 Benefits of MIMO 8.1.1 Array Gain 8.1.2 Diversity Gain 8.1.3 Multiplexing Gain 8.2 Space Time Coding 8.2.1 Space Time Block Coding 8.2.2 Space Time Trellis Coding 8.2.3 Layered Space Time Coding 8.3 Multi-User MIMO 8.3.1 Space-Division Multiple Access 8.3.2 MIMO Broadcast Channel 8.3.3 MIMO Multiple Access Channel 8.3.4 MIMO Interference Channel 8.4 MIMO Network 8.5 MIMO Cognitive Radio Network 8.6 Summary 9 Agile Transmission Techniques (II): Orthogonal Frequency Division Multiplexing 9.1 OFDM Implementation 9.2 Synchronization 9.3 Channel Estimation 9.4 Peak Power Problem 9.5 Adaptive Transmission 9.6 Spectrum Shaping 9.7 Orthogonal Frequency Division Multiple Access 9.8 MIMO OFDM 9.9 OFDM Cognitive Radio Network 9.10 Summary 10 Game Theory 10.1 Basic Concepts of Games 10.1.1 Elements of Games 10.1.2 Nash Equilibrium: Definition and Existence 10.1.3 Nash Equilibrium: Computation 10.1.4 Nash Equilibrium: Zero-Sum Games 10.1.5 Nash Equilibrium: Bayesian Case 10.1.6 Nash Equilibrium: Stochastic Games 10.2 Primary User Emulation Attack Games 10.2.1 PUE Attack 10.2.2 Two-Player Case: A Strategic-Form Game 10.2.3 Game in Queuing Dynamics: A Stochastic Game 10.3 Games in Channel Synchronization 10.3.1 Background of the Game 10.3.2 System Model 10.3.3 Game Formulation 10.3.4 Bayesian Equilibrium 10.3.5 Numerical Results 10.4 Games in Collaborative Spectrum Sensing 10.4.1 False Report Attack 10.4.2 Game Formulation 10.4.3 Elements of Game 10.4.4 Bayesian Equilibrium 10.4.5 Numerical Results 11 Cognitive Radio Network 11.1 Basic Concepts of Networks 11.1.1 Network Architecture 11.1.2 Network Layers 11.1.3 Cross-Layer Design 11.1.4 Main Challenges in Cognitive Radio Networks 11.1.5 Complex Networks 11.2 Channel Allocation in MAC Layer 11.2.1 Problem Formulation 11.2.2 Scheduling Algorithm 11.2.3 Solution 11.2.4 Discussion 11.3 Scheduling in MAC Layer 11.3.1 Network Model 11.3.2 Goal of Scheduling 11.3.3 Scheduling Algorithm 11.3.4 Performance of the CNC Algorithm 11.3.5 Distributed Scheduling Algorithm 11.4 Routing in Network Layer 11.4.1 Challenges of Routing in Cognitive Radio 11.4.2 Stationary Routing 11.4.3 Dynamic Routing 11.5 Congestion Control in Transport Layer 11.5.1 Congestion Control in Internet 11.5.2 Challenges in Cognitive Radio 11.5.3 TP-CRAHN 11.5.4 Early Start Scheme 11.6 Complex Networks in Cognitive Radio 11.6.1 Brief Introduction to Complex Networks 11.6.2 Connectivity of Cognitive Radio Networks 11.6.3 Behavior Propagation in Cognitive Radio Networks 12 Cognitive Radio Network as Sensors 12.1 Intrusion Detection by Machine Learning 12.2 Joint Spectrum Sensing and Localization 12.3 Distributed Aspect Synthetic Aperture Radar 12.4 Wireless Tomography 12.5 Mobile Crowdsensing 12.6 Integration of 3S 12.7 The Cyber-Physical System 12.8 Computing 12.8.1 Graphics Processor Unit 12.8.2 Task Distribution and Load Balancing 12.9 Security and Privacy 12.10 Summary Appendix A Matrix Analysis A.1 Vector Spaces and Hilbert Space A.2 Transformations A.3 Trace A.4 Basics of C ∗-Algebra A.5 Noncommunicative Matrix-Valued Random Variables A.6 Distances and Projections A.6.1 Matrix Inequalities A.6.2 Partial Ordering of Positive Semidefinite Matrices A.6.3 Partial Ordering of Hermitian Matrices References Index
Extra informatie: 
Hardback
534 pagina's
Januari 2012
953 gram
241 x 171 x 25 mm
Wiley-Blackwell us

Levertijd: 5 tot 11 werkdagen