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Signal Processing for Cognitive Radios

Book 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.

Table of Contents

  1. COVER
  2. TITLE PAGE
  3. COPYRIGHT PAGE
  4. DEDICATION PAGE
  5. PREFACE
  6. PART I: INTRODUCTION TO COGNITIVE RADIOS
    1. 1 INTRODUCTION
      1. 1.1 INTRODUCTION
      2. 1.2 SIGNAL PROCESSING AND COGNITIVE RADIOS
      3. 1.3 SOFTWARE-DEFINED RADIOS
      4. 1.4 FROM SOFTWARE-DEFINED RADIOS TO COGNITIVE RADIOS
      5. 1.5 WHAT THIS BOOK IS ABOUT
      6. 1.6 SUMMARY
    2. 2 THE COGNITIVE RADIO
      1. 2.1 INTRODUCTION
      2. 2.2 A FUNCTIONAL MODEL OF A COGNITIVE RADIO
      3. 2.3 THE COGNITIVE RADIO ARCHITECTURE
      4. 2.4 THE IDEAL COGNITIVE RADIO
      5. 2.5 SIGNAL PROCESSING CHALLENGES IN COGNITIVE RADIOS
      6. 2.6 SUMMARY
    3. 3 COGNITIVE RADIOS AND DYNAMIC SPECTRUM SHARING
      1. 3.1 INTRODUCTION
      2. 3.2 INTERFERENCE AND SPECTRUM OPPORTUNITIES
      3. 3.3 DYNAMIC SPECTRUM ACCESS
      4. 3.4 DYNAMIC SPECTRUM LEASING
      5. 3.5 CHALLENGES IN DSS COGNITIVE RADIOS
      6. 3.6 COGNITIVE RADIOS AND FUTURE OF WIRELESS COMMUNICATIONS
      7. 3.7 SUMMARY
  7. PART II: THEORETICAL FOUNDATIONS
    1. 4 INTRODUCTION TO DETECTION THEORY
      1. 4.1 INTRODUCTION
      2. 4.2 OPTIMALITY CRITERIA: BAYESIAN VERSUS NON-BAYESIAN
      3. 4.3 PARAMETRIC SIGNAL DETECTION THEORY
      4. 4.4 NONPARAMETRIC SIGNAL DETECTION THEORY
      5. 4.5 SUMMARY
    2. 5 INTRODUCTION TO ESTIMATION THEORY
      1. 5.1 INTRODUCTION
      2. 5.2 RANDOM PARAMETER ESTIMATION: BAYESIAN ESTIMATION
      3. 5.3 NONRANDOM PARAMETER ESTIMATION
      4. 5.4 SUMMARY
    3. 6 POWER SPECTRUM ESTIMATION
      1. 6.1 INTRODUCTION
      2. 6.2 PSD ESTIMATION OF A STATIONARY DISCRETE-TIME SIGNAL
      3. 6.3 BLACKMAN–TUKEY ESTIMATOR OF THE POWER SPECTRUM
      4. 6.4 OTHER PSD ESTIMATORS BASED ON MODIFIED PERIODOGRAMS
      5. 6.5 PSD ESTIMATION OF NONSTATIONARY DISCRETE-TIME SIGNALS
      6. 6.6 SPECTRAL CORRELATION OF CYCLOSTATIONARY SIGNALS
      7. 6.7 SUMMARY
    4. 7 MARKOV DECISION PROCESSES
      1. 7.1 INTRODUCTION
      2. 7.2 <i xmlns="http://www.w3.org/1999/xhtml" xmlns:epub="http://www.idpf.org/2007/ops" xmlns:m="http://www.w3.org/1998/Math/MathML" xmlns:svg="http://www.w3.org/2000/svg" xmlns:ibooks="http://vocabulary.itunes.apple.com/rdf/ibooks/vocabulary-extensions-1.0">MARKOV DECISION PROCESSES</i>
      3. 7.3 FINITE-HORIZON MDPs
      4. 7.4 INFINITE-HORIZON MDPs
      5. 7.5 PARTIALLY OBSERVABLE MARKOV DECISION PROCESSES
      6. 7.6 SUMMARY
    5. 8 BAYESIAN NONPARAMETRIC CLASSIFICATION
      1. 8.1 INTRODUCTION
      2. 8.2 <i xmlns="http://www.w3.org/1999/xhtml" xmlns:epub="http://www.idpf.org/2007/ops" xmlns:m="http://www.w3.org/1998/Math/MathML" xmlns:svg="http://www.w3.org/2000/svg" xmlns:ibooks="http://vocabulary.itunes.apple.com/rdf/ibooks/vocabulary-extensions-1.0">K</i>-MEANS CLASSIFICATION ALGORITHM-MEANS CLASSIFICATION ALGORITHM
      3. 8.3 <i xmlns="http://www.w3.org/1999/xhtml" xmlns:epub="http://www.idpf.org/2007/ops" xmlns:m="http://www.w3.org/1998/Math/MathML" xmlns:svg="http://www.w3.org/2000/svg" xmlns:ibooks="http://vocabulary.itunes.apple.com/rdf/ibooks/vocabulary-extensions-1.0">X</i>-MEANS CLASSIFICATION ALGORITHM-MEANS CLASSIFICATION ALGORITHM
      4. 8.4 DIRICHLET PROCESS MIXTURE MODEL
      5. 8.5 BAYESIAN NONPARAMETRIC CLASSIFICATION BASED ON THE DPMM AND THE GIBBS SAMPLING
      6. 8.6 SUMMARY
  8. PART III: SIGNAL PROCESSING IN COGNITIVE RADIOS
    1. 9 WIDEBAND SPECTRUM SENSING
      1. 9.1 INTRODUCTION
      2. 9.2 WIDEBAND SPECTRUM SENSING PROBLEM
      3. 9.3 WIDEBAND SPECTRUM SCANNING PROBLEM
      4. 9.4 SPECTRUM SEGMENTATION AND SUBBANDING
      5. 9.5 WIDEBAND SPECTRUM SENSING RECEIVER
      6. 9.6 SUBBAND SELECTION PROBLEM IN WIDEBAND SPECTRUM SENSING
      7. 9.7 A REDUCED COMPLEXITY OPTIMAL SUBBAND SELECTION FRAMEWORK WITH AN ALTERNATIVE REWARD FUNCTION
      8. 9.8 MACHINE-LEARNING AIDED SUBBAND SELECTION POLICIES
      9. 9.9 SUMMARY
    2. 10 SPECTRAL ACTIVITY DETECTION IN WIDEBAND COGNITIVE RADIOS
      1. 10.1 INTRODUCTION
      2. 10.2 OPTIMAL WIDEBAND SPECTRAL ACTIVITY DETECTION
      3. 10.3 WIDEBAND SPECTRAL ACTIVITY DETECTION
      4. 10.4 WAVELET TRANSFORM-BASED WIDEBAND SPECTRAL ACTIVITY DETECTION
      5. 10.5 WIDEBAND SPECTRAL ACTIVITY DETECTION IN NON-GAUSSIAN NOISE
      6. 10.6 WIDEBAND SPECTRAL ACTIVITY DETECTION WITH COMPRESSIVE SAMPLING
      7. 10.7 SUMMARY
    3. 11 SIGNAL CLASSIFICATION IN WIDEBAND COGNITIVE RADIOS
      1. 11.1 INTRODUCTION
      2. 11.2 SIGNAL CLASSIFICATION PROBLEM IN A WIDEBAND COGNITIVE RADIO
      3. 11.3 FEATURE EXTRACTION FOR SIGNAL CLASSIFICATION
      4. 11.4 A SIGNAL CLASSIFICATION ARCHITECTURE FOR A WIDEBAND COGNITIVE RADIO
      5. 11.5 BAYESIAN NONPARAMETRIC SIGNAL CLASSIFICATION
      6. 11.6 SEQUENTIAL BAYESIAN NONPARAMETRIC SIGNAL CLASSIFICATION
      7. 11.7 SUMMARY
    4. 12 PRIMARY SIGNAL DETECTION IN DSA COGNITIVE NETWORKS
      1. 12.1 INTRODUCTION
      2. 12.2 SPECTRUM SENSING PROBLEM IN DYNAMIC SPECTRUM SHARING CR NETWORKS
      3. 12.3 AUTONOMOUS SPECTRUM SENSING FOR DYNAMIC SPECTRUM SHARING
      4. 12.4 LIMITATIONS OF AUTONOMOUS SPECTRUM SENSING
      5. 12.5 COOPERATIVE SPECTRUM SENSING FOR DYNAMIC SPECTRUM SHARING
      6. 12.6 COOPERATIVE CHANNEL-STATE DETECTION
      7. 12.7 SUMMARY
    5. 13 SPECTRUM DECISION-MAKING IN DSA COGNITIVE NETWORKS
      1. 13.1 INTRODUCTION
      2. 13.2 PRIMARY CHANNEL DYNAMIC MODEL
      3. 13.3 SENSING DECISIONS IN DSS NETWORKS WITH AUTONOMOUS COGNITIVE RADIOS
      4. 13.4 SENSING DECISIONS IN COOPERATIVE DSS NETWORKS
      5. 13.5 SUMMARY
    6. 14 DYNAMIC SPECTRUM LEASING IN COGNITIVE RADIO NETWORKS
      1. 14.1 INTRODUCTION
      2. 14.2 DSL WITH DIRECT REWARDS TO PRIMARY USERS
      3. 14.3 DSL BASED ON ASYMMETRIC COOPERATION WITH PRIMARY USERS
      4. 14.4 SUMMARY
    7. 15 COOPERATIVE COGNITIVE COMMUNICATIONS
      1. 15.1 INTRODUCTION
      2. 15.2 COOPERATIVE SPECTRUM SENSING
      3. 15.3 COOPERATIVE SPECTRUM SENSING AND CHANNEL-ACCESS DECISIONS
      4. 15.4 COOPERATIVE COMMUNICATIONS STRATEGIES IN COGNITIVE RADIO NETWORKS
      5. 15.5 ASYMMETRIC COOPERATIVE RELAYING IN DSA COGNITIVE RADIOS
      6. 15.6 SUMMARY
    8. 16 MACHINE LEARNING IN COGNITIVE RADIOS
      1. 16.1 INTRODUCTION
      2. 16.2 ARTIFICIAL NEURAL NETWORKS
      3. 16.3 SUPPORT VECTOR MACHINES
      4. 16.4 REINFORCEMENT LEARNING
      5. 16.5 MULTIAGENT LEARNING
      6. 16.6 SUMMARY
  9. APPENDIX A: NYQUIST SAMPLING THEOREM
  10. APPENDIX B: A COLLECTION OF USEFUL PROBABILITY DISTRIBUTIONS
    1. B.1 UNIVARIATE DISTRIBUTIONS
    2. B.2 MULTIVARIATE DISTRIBUTIONS
  11. APPENDIX C: CONJUGATE PRIORS
  12. REFERENCES
  13. INDEX
  14. END USER LICENSE AGREEMENT