Robert Caiming Qiu, Zhen Hu, Husheng Li, Michael C Wicks

Cognitive Radio Communication and Networking

Principles and Practice
By Qiu, Robert Caiming; Hu, Zhen; Li, Husheng; Wicks, Michael C.

• Gebundenes Buch

Produktbeschreibung

The author presents a unified treatment of this highly interdisciplinary topic to help define the notion of cognitive radio. The book begins with addressing issues such as the fundamental system concept and basic mathematical tools such as spectrum sensing and machine learning, before moving on to more advanced concepts and discussions about the future of cognitive radio. From the fundamentals in spectrum sensing to the applications of cognitive algorithms to radio communications, and discussion of radio platforms and testbeds to show the applicability of the theory to practice, the author aims to provide an introduction to a fast moving topic for students and researchers seeking to develop a thorough understanding of cognitive radio networks.
Examines basic mathematical tools before moving on to more advanced concepts and discussions about the future of cognitive radio
Describe the fundamentals of cognitive radio, providing a step by step treatment of the topics to enable progressive learning
Includes questions, exercises and suggestions for extra reading at the end of each chapter
Companion website hosting MATLAB codes, and supplementary material including exercises

Topics covered in the book include: Spectrum Sensing: Basic Techniques; Cooperative Spectrum Sensing Wideband Spectrum Sensing; Agile Transmission Techniques: Orthogonal Frequency Division Multiplexing Multiple Input Multiple Output for Cognitive Radio; Convex Optimization for Cognitive Radio; Cognitive Core (I): Algorithms for Reasoning and Learning; Cognitive Core (II): Game Theory; Cognitive Radio Network IEEE 802.22: The First Cognitive Radio Wireless Regional Area Network Standard, and Radio Platforms and Testbeds.

Produktdetails

• Verlag: Wiley & Sons
• 1. Auflage
• Seitenzahl: 534
• Erscheinungstermin: 5. November 2012
• Englisch
• Abmessung: 251mm x 177mm x 30mm
• Gewicht: 948g
• ISBN-13: 9780470972090
• ISBN-10: 0470972092
• Artikelnr.: 36519418

Autorenporträt

Robert C. Qiu, Department of Electrical and Computer Engineering, Tennessee Technological University, USA Professor Qiu is currently Director of the Wireless Networking System Laboratory at Tennessee Technological University, USA. He was Founder-CEO and President of Wiscom Technologies, Inc., manufacturing and marketing WCDMA chipsets. Wiscom was acquired by Intel in 2003. Prior to Wiscom, he worked for GTE Labs, Inc. (now Verizon), Waltham, MA, and Bell Labs, Lucent, Whippany, NJ. He holds 5 U.S. patents (another two pending) in WCDMA. He is co-editor of Ultra-Wideband Wireless Communications and Networks (Wiley), and? has authored over 80 technical (journal/conference) papers and contributed 6 book chapters. Professor Qiu has contributed to 3GPP and IEEE standards bodies, and delivered invited seminars to institutions including Princeton University and the U.S. Army Research Lab. Dr. Qiu serves as Associate Editor, IEEE Transaction on Vehicular Technology, International Journal of Sensor Networks and Wireless Communication and Mobile Computing.

Inhaltsangabe

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