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Signal Processing for Cognitive Radios

Signal Processing for Cognitive Radios

Sudharman K. Jayaweera

ISBN: 978-1-118-82481-8

Nov 2014

768 pages

Description

This book examines signal processing techniques for cognitive radios. The book is divided into three parts:

Part I, is an introduction to cognitive radios and presents a history of the cognitive radio (CR), and introduce their architecture, functionalities, ideal aspects, hardware platforms, and state-of-the-art developments. Dr. Jayaweera also introduces the specific type of CR that has gained the most research attention in recent years: the CR for Dynamic Spectrum Access (DSA).

Part II of the book, Theoretical Foundations, guides the reader from classical to modern theories on statistical signal processing and inference. The author addresses detection and estimation theory, power spectrum estimation, classification, adaptive algorithms (machine learning), and inference and decision processes. Applications to the signal processing, inference and learning problems encountered in cognitive radios are interspersed throughout with concrete and accessible examples.

Part III of the book, Signal Processing in Radios, identifies the key signal processing, inference, and learning tasks to be performed by wideband autonomous cognitive radios. The author provides signal processing solutions to each task by relating the tasks to materials covered in Part II. Specialized chapters then discuss specific signal processing algorithms required for DSA and DSS cognitive radios.

PREFACE xv

PART I INTRODUCTION TO COGNITIVE RADIOS 1

1 Introduction 3

1.1 Introduction, 3

1.2 Signal Processing and Cognitive Radios, 4

1.3 Software-Defined Radios, 6

1.3.1 Software-Defined Radio Platforms, 14

1.3.2 Software-Defined Radio Systems, 15

1.4 From Software-Defined Radios to Cognitive Radios, 19

1.4.1 The Spectrum Scarcity Problem, 19

1.4.2 Emergence of CRs, 21

1.5 What this Book is About, 22

1.6 Summary, 26

2 The Cognitive Radio 27

2.1 Introduction, 27

2.2 A Functional Model of a Cognitive Radio, 30

2.2.1 Spectrum Knowledge Acquisition (Spectrum Awareness), 30

2.2.2 Communications Decision-Making, 33

2.2.3 Learning in Cognitive Radios, 33

2.3 The Cognitive Radio Architecture, 35

2.3.1 Spectrum Sensing Region of a Cognitive Engine, 36

2.3.2 Radio Reconfiguration Region of a Cognitive Engine, 36

2.3.3 Learning Region of a Cognitive Engine, 37

2.3.4 Memory Region of a Cognitive Engine, 37

2.4 The Ideal Cognitive Radio, 38

2.5 Signal Processing Challenges in Cognitive Radios, 39

2.6 Summary, 40

3 Cognitive Radios and Dynamic Spectrum Sharing 42

3.1 Introduction, 42

3.2 Interference and Spectrum Opportunities, 46

3.3 Dynamic Spectrum Access, 50

3.4 Dynamic Spectrum Leasing, 54

3.5 Challenges in DSS Cognitive Radios, 55

3.6 Cognitive Radios and Future of Wireless Communications, 60

3.7 Summary, 61

PART II THEORETICAL FOUNDATIONS 65

4 Introduction to Detection Theory 67

4.1 Introduction, 67

4.2 Optimality Criteria: Bayesian versus Non-Bayesian, 71

4.2.1 The Bayesian Approach, 72

4.2.2 A Non-Bayesian Approach: Neyman–Pearson Optimality Criterion, 73

4.3 Parametric Signal Detection Theory, 75

4.3.1 Bayesian Optimal Detection, 76

4.3.2 Neyman–Pearson Optimal Detection, 82

4.3.3 Another Non-Bayesian Alternative: The Generalized Likelihood Ratio Test, 99

4.3.4 Parametric Signal Detection in Additive Noise, 103

4.4 Nonparametric Signal Detection Theory, 122

4.4.1 Signal Detection in Additive Zero-Median Noise: The Sign Test, 124

4.4.2 Signal Detection in Additive Symmetric Noise: The Rank Test, 125

4.4.3 Signal Detection in Additive Zero Median, Zero Mean, Finite-Variance Noise: The t-Test, 126

4.5 Summary, 127

5 Introduction to Estimation Theory 132

5.1 Introduction, 132

5.2 Random Parameter Estimation: Bayesian Estimation, 134

5.2.1 Minimum Mean-Squared Error Estimation, 134

5.2.2 MMSE Estimation of Vector Parameters, 135

5.2.3 Linear Minimum Mean-Squared Error Estimation, 138

5.2.4 Maximum A Posteriori Probability Estimation, 139

5.3 Nonrandom Parameter Estimation, 140

5.3.1 Theory of Minimum Variance Unbiased Estimation, 142

5.3.2 Best Linear Unbiased Estimator, 147

5.3.3 Maximum Likelihood Estimation, 152

5.3.4 Performance Bounds: Cramer-Rao Lower Bound, 154

5.4 Summary, 158

6 Power Spectrum Estimation 164

6.1 Introduction, 164

6.2 PSD Estimation of a Stationary Discrete-Time Signal, 168

6.2.1 Correlogram Method, 168

6.2.2 Periodogram Method, 170

6.2.3 Performance of the Periodogram PSD Estimate, 172

6.3 Blackman–Tukey Estimator of the Power Spectrum, 177

6.4 Other PSD Estimators Based on Modified Periodograms, 181

6.4.1 Bartlett PSD Estimator, 181

6.4.2 Welch PSD Estimator, 183

6.5 PSD Estimation of Nonstationary Discrete-Time Signals, 186

6.5.1 Temporally Windowed Observations, 188

6.5.2 Temporal and Spectral Smoothing of PSD Estimates of Nonstationary Discrete-Time Signals, 189

6.5.3 DFT-Based PSD Computation, 191

6.6 Spectral Correlation of Cyclostationary Signals, 192

6.6.1 Spectral Correlation and Spectral Autocoherence, 196

6.6.2 Time-Averaged Spectral Correlation, 197

6.6.3 Estimation of Spectral Correlation, 198

6.7 Summary, 200

7 Markov Decision Processes 207

7.1 Introduction, 207

7.2 Markov Decission Processes, 209

7.3 Finite-Horizon MDPs, 212

7.3.1 Definitions, 212

7.3.2 Optimal Policies for MDPs, 216

7.4 Infinite-Horizon MDPs, 222

7.4.1 Stationary Optimal Policies for Infinite-Horizon MDPs, 224

7.4.2 Bellman-Optimality Equations, 227

7.5 Partially Observable Markov Decision Processes, 232

7.5.1 Definitions, 233

7.5.2 Policy Evaluation for a Finite-Horizon POMDP, 238

7.5.3 Optimality Equations for a Finite-Horizon POMDP, 241

7.5.4 Optimal Policy Computation for a Finite-Horizon POMDP, 242

7.5.5 Infinite-Horizon POMDPs, 257

7.6 Summary, 259

8 Bayesian Nonparametric Classification 269

8.1 Introduction, 269

8.2 K-Means Classification Algorithm, 274

8.3 X-Means Classification Algorithm, 276

8.4 Dirichlet Process Mixture Model, 278

8.4.1 Dirichlet Process, 278

8.4.2 Construction of the Dirichlet Process, 279

8.4.3 DPMM, 282

8.5 Bayesian Nonparametric Classification Based on the DPMM and the Gibbs Sampling, 283

8.5.1 DPMM-Based Classification of Scalar Observations, 287

8.5.2 DPMM-Based Classification of Multidimensional Gaussian Observations, 298

8.5.3 DPMM-Based Classification of Possibly Non-Gaussian Multidimensional Observations, 308

8.6 Summary, 315

PART III SIGNAL PROCESSING IN COGNITIVE RADIOS 321

9 Wideband Spectrum Sensing 323

9.1 Introduction, 323

9.2 Wideband Spectrum Sensing Problem, 325

9.3 Wideband Spectrum Scanning Problem, 326

9.4 Spectrum Segmentation and Subbanding, 328

9.5 Wideband Spectrum Sensing Receiver, 330

9.5.1 Homodyne Receiver Configuration, 332

9.5.2 Super Heterodyne Digital Receiver Configuration, 334

9.5.3 A/D Conversion and the Discrete-Time Received Signal Model, 335

9.6 Subband Selection Problem in Wideband Spectrum Sensing, 336

9.6.1 Subband Dynamics, 338

9.6.2 A POMDP Model for Subband Selection, 340

9.6.3 An Optimal Subband Selection Policy for Spectrum Sensing, 347

9.6.4 A Reduced-Complexity Optimal Sensing Decision-Making Algorithm with Independent Channels, 350

9.6.5 A Reduced Complexity Optimal Sensing Decision-Making Algorithm with Independent Subbands, 354

9.6.6 Optimal Myopic Sensing Decision Policies, 354

9.7 A Reduced Complexity Optimal Subband Selection Framework with an Alternative Reward Function, 355

9.7.1 A New Model for Subband Dynamics, 357

9.7.2 A Simplified Reward Function and a Reduced-Complexity Optimal Policy, 359

9.7.3 A Reduced Complexity Optimal Policy for Independent Subbands, 362

9.7.4 Optimal Myopic Policies with Reduced Dimensional Subband State Vectors, 363

9.8 Machine-Learning Aided Subband Selection Policies, 364

9.8.1 Q-Learning, 365

9.8.2 Q-Learning in a POMDP: A Q-Learning Algorithm for Subband Selection, 368

9.9 Summary, 372

10 Spectral Activity Detection inWideband Cognitive Radios 377

10.1 Introduction, 377

10.2 Optimal Wideband Spectral Activity Detection, 379

10.3 Wideband Spectral Activity Detection, 386

10.4 Wavelet Transform-Based Wideband Spectral Activity Detection, 392

10.4.1 Wavelet Transform, 394

10.4.2 Edge Detection with Wavelet Transform, 395

10.4.3 Spectral Activity Detection Based on Edge Detection, 397

10.5 Wideband Spectral Activity Detection in Non-Gaussian Noise, 398

10.5.1 Arbitrary but Known Noise Distribution, 399

10.5.2 Robust Spectral Activity Detection, 406

10.6 Wideband Spectral Activity Detection with Compressive Sampling, 413

10.6.1 Compressive Sampling, 415

10.6.2 Compressive Sensing of Wideband Spectrum, 419

10.7 Summary, 421

11 Signal Classification inWideband Cognitive Radios 429

11.1 Introduction, 429

11.2 Signal Classification Problem in a Wideband Cognitive Radio, 431

11.3 Feature Extraction for Signal Classification, 435

11.3.1 Carrier/Center Frequency, 435

11.3.2 Cyclostationary Features, 436

11.3.3 Modulation Type and Order Features, 441

11.4 A Signal Classification Architecture for a Wideband Cognitive Radio, 445

11.5 Bayesian Nonparametric Signal Classification, 447

11.6 Sequential Bayesian Nonparametric Signal Classification, 462

11.7 Summary, 469

12 Primary Signal Detection in DSA Cognitive Networks 472

12.1 Introduction, 472

12.2 Spectrum Sensing Problem in Dynamic Spectrum Sharing CR Networks, 475

12.3 Autonomous Spectrum Sensing for Dynamic Spectrum Sharing, 479

12.3.1 Secondary User Sensing Observations, 480

12.3.2 Channel-State (Idle/Busy) Decisions, 481

12.4 Limitations of Autonomous Spectrum Sensing, 489

12.5 Cooperative Spectrum Sensing for Dynamic Spectrum Sharing, 492

12.6 Cooperative Channel-State Detection, 495

12.6.1 Local Processing and Sensing Reports from Secondary Users, 498

12.6.2 Final Channel-State Decisions at the SSDC: Decision Fusion, 502

12.7 Summary, 516

13 Spectrum Decision-Making in DSA Cognitive Networks 519

13.1 Introduction, 519

13.2 Primary Channel Dynamic Model, 520

13.3 Sensing Decisions in DSS Networks with Autonomous Cognitive Radios, 522

13.3.1 Optimal Sensing Policy Determination, 525

13.3.2 Optimal Myopic Sensing Policy Determination, 530

13.4 Sensing Decisions in Cooperative DSS Networks, 533

13.4.1 Optimal SSDC Decisions for Independent Channel Dynamics, 537

13.4.2 Optimal Myopic Sensing Decisions at the SSDC with Independent Channel Dynamics, 541

13.5 Summary, 550

14 Dynamic Spectrum Leasing in Cognitive Radio Networks 553

14.1 Introduction, 553

14.2 DSL with Direct Rewards to Primary Users, 555

14.2.1 Interference at the Primary Receiver, 560

14.2.2 A Game Model for Dynamic Spectrum Leasing, 565

14.2.3 Nash Equilibria in Noncooperative Games, 570

14.2.4 Existence of a Nash Equilibrium in the DSL Game, 573

14.3 DSL Based on Asymmetric Cooperation with Primary Users, 587

14.3.1 A Primary–Secondary Coexistence Model, 588

14.3.2 Asymmetric Cooperative Communications-Based DSL between Primary Users and a Centralized Secondary Network, 591

14.3.3 Asymmetric Cooperative Communications-Based DSL between Primary Users and Autonomous Cognitive Secondary Users, 604

14.4 Summary, 609

15 Cooperative Cognitive Communications 613

15.1 Introduction, 613

15.2 Cooperative Spectrum Sensing, 619

15.3 Cooperative Spectrum Sensing and Channel-Access Decisions, 621

15.4 Cooperative Communications Strategies in Cognitive Radio Networks, 624

15.5 Asymmetric Cooperative Relaying in DSA Cognitive Radios, 627

15.5.1 Secondary User Optimal Power Allocation for Asymmetric Cooperative Relaying, 629

15.5.2 Centralized Assignment of Cognitive Radios for Cooperation with Primary Users: An Ideal Approach, 635

15.5.3 Centralized Assignment of Cognitive Radios for Cooperation with Primary Users: A Realistic Approach, 640

15.6 Summary, 644

16 Machine Learning in Cognitive Radios 647

16.1 Introduction, 647

16.2 Artificial Neural Networks, 650

16.2.1 Learning Algorithms for LTUs, 651

16.2.2 Layered Neural Networks, 655

16.2.3 Learning in Layered Feed-Forward Networks: Back-Propagation Algorithm, 656

16.2.4 Neural Networks in Cognitive Radios, 662

16.3 Support Vector Machines, 664

16.3.1 Statistical Learning Theory, 665

16.3.2 Structural Risk Minimization with Support Vector Machines, 669

16.3.3 Linear Support Vector Machines, 670

16.3.4 Nonlinear Support Vector Machines, 674

16.3.5 Kernel Function Implementation of Support Vector Machines, 677

16.3.6 SVMs in Cognitive Radios, 679

16.4 Reinforcement Learning, 681

16.4.1 Temporal Difference Learning, 683

16.4.2 Q-Learning in a POMDP: Replicated Q-Learning, 684

16.4.3 Reinforcement Learning in Cognitive Radios, 686

16.5 Multiagent Learning, 688

16.5.1 Game-Theoretic Multiagent Learning, 691

16.5.2 Cooperative Multiagent Learning, 694

16.5.3 Multiagent Learning in Cognitive Radio Networks, 696

16.6 Summary, 698

Appendix A Nyquist Sampling Theorem 704

Appendix B A Collection of Useful Probability Distributions 711

B.1 Univariate Distributions, 711

B.2 Multivariate Distributions, 713

Appendix C Conjugate Priors 716

REFERENCES 721

INDEX 740