Signal Processing for Cognitive Radios -- Copyright -- Contents -- Preface -- Part I Introduction to Cognitive Radios -- Chapter 1 Introduction -- 1.1 Introduction -- 1.2 Signal Processing and Cognitive Radios -- 1.3 Software-Defined Radios -- 1.3.1 Software-Defined Radio Platforms -- 1.3.2 Software-Defined Radio Systems -- 1.4 From Software-Defined Radios to Cognitive Radios -- 1.4.1 The Spectrum Scarcity Problem -- 1.4.2 Emergence of CRs -- 1.5 What this Book is About -- 1.6 Summary -- Chapter 2 The Cognitive Radio -- 2.1 Introduction -- 2.2 A Functional Model of a Cognitive Radio -- 2.2.1 Spectrum Knowledge Acquisition (Spectrum Awareness) -- 2.2.2 Communications Decision-Making -- 2.2.3 Learning in Cognitive Radios -- 2.3 The Cognitive Radio Architecture -- 2.3.1 Spectrum Sensing Region of a Cognitive Engine -- 2.3.2 Radio Reconfiguration Region of a Cognitive Engine -- 2.3.3 Learning Region of a Cognitive Engine -- 2.3.4 Memory Region of a Cognitive Engine -- 2.4 The Ideal Cognitive Radio -- 2.5 Signal Processing Challenges in Cognitive Radios -- 2.6 Summary -- Chapter 3 Cognitive Radios and Dynamic Spectrum Sharing -- 3.1 Introduction -- 3.2 Interference and Spectrum Opportunities -- 3.3 Dynamic Spectrum Access -- 3.4 Dynamic Spectrum Leasing -- 3.5 Challenges in DSS Cognitive Radios -- 3.6 Cognitive Radios and Future of Wireless Communications -- 3.7 Summary -- Part II Theoretical Foundations -- Chapter 4 Introduction to Detection Theory -- 4.1 Introduction -- 4.2 Optimality Criteria: Bayesian versus Non-Bayesian -- 4.2.1 The Bayesian Approach -- 4.2.2 A Non-Bayesian Approach: Neyman-Pearson Optimality Criterion -- 4.3 Parametric Signal Detection Theory -- 4.3.1 Bayesian Optimal Detection -- 4.3.2 Neyman-Pearson Optimal Detection -- 4.3.3 Another Non-Bayesian Alternative: The Generalized Likelihood Ratio Test.

4.3.4 Parametric Signal Detection in Additive Noise -- 4.4 Nonparametric Signal Detection Theory -- 4.4.1 Signal Detection in Additive Zero-Median Noise: The Sign Test -- 4.4.2 Signal Detection in Additive Symmetric Noise: The Rank Test -- 4.4.3 Signal Detection in Additive Zero Median, Zero Mean, Finite-Variance Noise: The t-Test -- 4.5 Summary -- Chapter 5 Introduction to Estimation Theory -- 5.1 Introduction -- 5.2 Random Parameter Estimation: Bayesian Estimation -- 5.2.1 Minimum Mean-Squared Error Estimation -- 5.2.2 MMSE Estimation of Vector Parameters -- 5.2.3 Linear Minimum Mean-Squared Error Estimation -- 5.2.4 Maximum A Posteriori Probability Estimation -- 5.3 Nonrandom Parameter Estimation -- 5.3.1 Theory of Minimum Variance Unbiased Estimation -- 5.3.2 Best Linear Unbiased Estimator -- 5.3.3 Maximum Likelihood Estimation -- 5.3.4 Performance Bounds: Cramer-Rao Lower Bound -- 5.4 Summary -- Chapter 6 Power Spectrum Estimation -- 6.1 Introduction -- 6.2 PSD Estimation of a Stationary Discrete-Time Signal -- 6.2.1 Correlogram Method -- 6.2.2 Periodogram Method -- 6.2.3 Performance of the Periodogram PSD Estimate -- 6.3 Blackman-Tukey Estimator of the Power Spectrum -- 6.4 Other PSD Estimators Based on Modified Periodograms -- 6.4.1 Bartlett PSD Estimator -- 6.4.2 Welch PSD Estimator -- 6.5 PSD Estimation of Nonstationary Discrete-Time Signals -- 6.5.1 Temporally Windowed Observations -- 6.5.2 Temporal and Spectral Smoothing of PSD Estimates of Nonstationary Discrete-Time Signals -- 6.5.3 DFT-Based PSD Computation -- 6.6 Spectral Correlation of Cyclostationary Signals -- 6.6.1 Spectral Correlation and Spectral Autocoherence -- 6.6.2 Time-Averaged Spectral Correlation -- 6.6.3 Estimation of Spectral Correlation -- 6.7 Summary -- Chapter 7 Markov Decision Processes -- 7.1 Introduction -- 7.2 Markov Decission Processes -- 7.3 Finite-Horizon MDPs.

7.3.1 Definitions -- 7.3.2 Optimal Policies for MDPs -- 7.4 Infinite-Horizon MDPs -- 7.4.1 Stationary Optimal Policies for Infinite-Horizon MDPs -- 7.4.2 Bellman-Optimality Equations -- 7.5 Partially Observable Markov Decision Processes -- 7.5.1 Definitions -- 7.5.2 Policy Evaluation for a Finite-Horizon POMDP -- 7.5.3 Optimality Equations for a Finite-Horizon POMDP -- 7.5.4 Optimal Policy Computation for a Finite-Horizon POMDP -- 7.5.5 Infinite-Horizon POMDPs -- 7.6 Summary -- Chapter 8 Bayesian Nonparametric Classification -- 8.1 Introduction -- 8.2 K-Means Classification Algorithm -- 8.3 X-Means Classification Algorithm -- 8.4 Dirichlet Process Mixture Model -- 8.4.1 Dirichlet Process -- 8.4.2 Construction of the Dirichlet Process -- 8.4.3 DPMM -- 8.5 Bayesian Nonparametric Classification Based on the DPMM and the Gibbs Sampling -- 8.5.1 DPMM-Based Classification of Scalar Observations -- 8.5.2 DPMM-Based Classification of Multidimensional Gaussian Observations -- 8.5.3 DPMM-Based Classification of Possibly Non-Gaussian Multidimensional Observations -- 8.6 Summary -- Part III Signal Processing in Cognitive Radios -- Chapter 9 Wideband Spectrum Sensing -- 9.1 Introduction -- 9.2 Wideband Spectrum Sensing Problem -- 9.3 Wideband Spectrum Scanning Problem -- 9.4 Spectrum Segmentation and Subbanding -- 9.5 Wideband Spectrum Sensing Receiver -- 9.5.1 Homodyne Receiver Configuration -- 9.5.2 Super Heterodyne Digital Receiver Configuration -- 9.5.3 A/D Conversion and the Discrete-Time Received Signal Model -- 9.6 Subband Selection Problem in Wideband Spectrum Sensing -- 9.6.1 Subband Dynamics -- 9.6.2 A POMDP Model for Subband Selection -- 9.6.3 An Optimal Subband Selection Policy for Spectrum Sensing -- 9.6.4 A Reduced-Complexity Optimal Sensing Decision-Making Algorithm with Independent Channels.

9.6.5 A Reduced Complexity Optimal Sensing Decision-Making Algorithm with Independent Subbands -- 9.6.6 Optimal Myopic Sensing Decision Policies -- 9.7 A Reduced Complexity Optimal Subband Selection Framework with an Alternative Reward Function -- 9.7.1 A New Model for Subband Dynamics -- 9.7.2 A Simplified Reward Function and a Reduced-Complexity Optimal Policy -- 9.7.3 A Reduced Complexity Optimal Policy for Independent Subbands -- 9.7.4 Optimal Myopic Policies with Reduced Dimensional Subband State Vectors -- 9.8 Machine-Learning Aided Subband Selection Policies -- 9.8.1 Q-Learning -- 9.8.2 Q-Learning in a POMDP: A Q-Learning Algorithm for Subband Selection -- 9.9 Summary -- Chapter 10 Spectral Activity Detection inWideband Cognitive Radios -- 10.1 Introduction -- 10.2 Optimal Wideband Spectral Activity Detection -- 10.3 Wideband Spectral Activity Detection -- 10.4 Wavelet Transform-Based Wideband Spectral Activity Detection -- 10.4.1 Wavelet Transform -- 10.4.2 Edge Detection withWavelet Transform -- 10.4.3 Spectral Activity Detection Based on Edge Detection -- 10.5 Wideband Spectral Activity Detection in Non-Gaussian Noise -- 10.5.1 Arbitrary but Known Noise Distribution -- 10.5.2 Robust Spectral Activity Detection -- 10.6 Wideband Spectral Activity Detection with Compressive Sampling -- 10.6.1 Compressive Sampling -- 10.6.2 Compressive Sensing of Wideband Spectrum -- 10.7 Summary -- Chapter 11 Signal Classification inWideband Cognitive Radios -- 11.1 Introduction -- 11.2 Signal Classification Problem in a Wideband Cognitive Radio -- 11.3 Feature Extraction for Signal Classification -- 11.3.1 Carrier/Center Frequency -- 11.3.2 Cyclostationary Features -- 11.3.3 Modulation Type and Order Features -- 11.4 A Signal Classification Architecture for a Wideband Cognitive Radio -- 11.5 Bayesian Nonparametric Signal Classification.

11.6 Sequential Bayesian Nonparametric Signal Classification -- 11.7 Summary -- Chapter 12 Primary Signal Detection in DSA Cognitive Networks -- 12.1 Introduction -- 12.2 Spectrum Sensing Problem in Dynamic Spectrum Sharing CR Networks -- 12.3 Autonomous Spectrum Sensing for Dynamic Spectrum Sharing -- 12.3.1 Secondary User Sensing Observations -- 12.3.2 Channel-State (Idle/Busy) Decisions -- 12.4 Limitations of Autonomous Spectrum Sensing -- 12.5 Cooperative Spectrum Sensing for Dynamic Spectrum Sharing -- 12.6 Cooperative Channel-State Detection -- 12.6.1 Local Processing and Sensing Reports from Secondary Users -- 12.6.2 Final Channel-State Decisions at the SSDC: Decision Fusion -- 12.7 Summary -- Chapter 13 Spectrum Decision-Making in DSA Cognitive Networks -- 13.1 Introduction -- 13.2 Primary Channel Dynamic Model -- 13.3 Sensing Decisions in DSS Networks with Autonomous Cognitive Radios -- 13.3.1 Optimal Sensing Policy Determination -- 13.3.2 Optimal Myopic Sensing Policy Determination -- 13.4 Sensing Decisions in Cooperative DSS Networks -- 13.4.1 Optimal SSDC Decisions for Independent Channel Dynamics -- 13.4.2 Optimal Myopic Sensing Decisions at the SSDC with Independent Channel Dynamics -- 13.5 Summary -- Chapter 14 Dynamic Spectrum Leasing in Cognitive Radio Networks -- 14.1 Introduction -- 14.2 DSL with Direct Rewards to Primary Users -- 14.2.1 Interference at the Primary Receiver -- 14.2.2 A Game Model for Dynamic Spectrum Leasing -- 14.2.3 Nash Equilibria in Noncooperative Games -- 14.2.4 Existence of a Nash Equilibrium in the DSL Game -- 14.3 DSL Based on Asymmetric Cooperation with Primary Users -- 14.3.1 A Primary-Secondary Coexistence Model -- 14.3.2 Asymmetric Cooperative Communications-Based DSL between Primary Users and a Centralized Secondary Network.

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