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Signal processing for cognitive radios / Sudharman K. Jayaweera.

By: Jayaweera, Sudharman K, 1972-.
Contributor(s): Wiley [publisher.] | IEEE Xplore (Online Service) [distributor.].
Material type: materialTypeLabelBookPublisher: Hoboken, New Jersey : John Wiley & Sons, Inc., [2015]Distributor: [Piscataqay, New Jersey] : IEEE Xplore, [2014]Description: 1 PDF (xvi, 747 pages) : illustrations.Content type: text Media type: electronic Carrier type: online resourceISBN: 9781118824818.Subject(s): Cognitive radio networks | Signal processingGenre/Form: Electronic books.DDC classification: 621.382/2 Online resources: Abstract with links to resource Also available in print.
Contents:
-- 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.
Summary: "This book covers power electronics, in depth, by presenting the basic principles and application details, and it can be used both as a textbook and reference book. Introduces the specific type of CR that has gained the most research attention in recent years: the CR for Dynamic Spectrum Access (DSA). 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 "-- Provided by publisher.
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Includes bibliographical references (pages 721-739) and index.

-- 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.

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"This book covers power electronics, in depth, by presenting the basic principles and application details, and it can be used both as a textbook and reference book. Introduces the specific type of CR that has gained the most research attention in recent years: the CR for Dynamic Spectrum Access (DSA). 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 "-- Provided by publisher.

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