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Cognitive Radio Communication and Networking: Principles and Practice

Book Description

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.

Note: The ebook version does not provide access to the companion files.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright
  4. Dedication
  5. Preface
  6. Chapter 1: Introduction
    1. 1.1 Vision: “Big Data”
    2. 1.2 Cognitive Radio: System Concepts
    3. 1.3 Spectrum Sensing Interface and Data Structures
    4. 1.4 Mathematical Machinery
    5. 1.5 Sample Covariance Matrix
    6. 1.6 Large Sample Covariance Matrices of Spiked Population Models
    7. 1.7 Random Matrices and Noncommutative Random Variables
    8. 1.8 Principal Component Analysis
    9. 1.9 Generalized Likelihood Ratio Test (GLRT)
    10. 1.10 Bregman Divergence for Matrix Nearness
  7. Chapter 2: Spectrum Sensing: Basic Techniques
    1. 2.1 Challenges
    2. 2.2 Energy Detection: No Prior Information about Deterministic or Stochastic Signal
    3. 2.3 Spectrum Sensing Exploiting Second-Order Statistics
    4. 2.4 Statistical Pattern Recognition: Exploiting Prior Information about Signal through Machine Learning
    5. 2.5 Feature Template Matching
    6. 2.6 Cyclostationary Detection
  8. Chapter 3: Classical Detection
    1. 3.1 Formalism of Quantum Information
    2. 3.2 Hypothesis Detection for Collaborative Sensing
    3. 3.3 Sample Covariance Matrix
    4. 3.4 Random Matrices with Independent Rows
    5. 3.5 The Multivariate Normal Distribution
    6. 3.6 Sample Covariance Matrix Estimation and Matrix Compressed Sensing
    7. 3.7 Likelihood Ratio Test
  9. Chapter 4: Hypothesis Detection of Noncommutative Random Matrices
    1. 4.1 Why Noncommutative Random Matrices?
    2. 4.2 Partial Orders of Covariance Matrices: A < B
    3. 4.3 Partial Ordering of Completely Positive Mappings: Φ(A) < Φ(B)
    4. 4.4 Partial Ordering of Matrices Using Majorization: A < B
    5. 4.5 Partial Ordering of Unitarily Invariant Norms: |||A||| < |||B|||
    6. 4.6 Partial Ordering of Positive Definite Matrices of Many Copies:
    7. 4.7 Partial Ordering of Positive Operator Valued Random Variables: Prob(A ≤ X ≤ B)
    8. 4.8 Partial Ordering Using Stochastic Order: A ≤ stB
    9. 4.9 Quantum Hypothesis Detection
    10. 4.10 Quantum Hypothesis Testing for Many Copies
  10. Chapter 5: Large Random Matrices
    1. 5.1 Large Dimensional Random Matrices: Moment Approach, Stieltjes Transform and Free Probability
    2. 5.2 Spectrum Sensing Using Large Random Matrices
    3. 5.3 Moment Approach
    4. 5.4 Stieltjes Transform
    5. 5.5 Case Studies and Applications
    6. 5.6 Regularized Estimation of Large Covariance Matrices
    7. 5.7 Free Probability
  11. Chapter 6: Convex Optimization
    1. 6.1 Linear Programming
    2. 6.2 Quadratic Programming
    3. 6.3 Semidefinite Programming
    4. 6.4 Geometric Programming
    5. 6.5 Lagrange Duality
    6. 6.6 Optimization Algorithm
    7. 6.7 Robust Optimization
    8. 6.8 Multiobjective Optimization
    9. 6.9 Optimization for Radio Resource Management
    10. 6.10 Examples and Applications
    11. 6.11 Summary
  12. Chapter 7: Machine Learning
    1. 7.1 Unsupervised Learning
    2. 7.2 Supervised Learning
    3. 7.3 Semisupervised Learning
    4. 7.4 Transductive Inference
    5. 7.5 Transfer Learning
    6. 7.6 Active Learning
    7. 7.7 Reinforcement Learning
    8. 7.8 Kernel-Based Learning
    9. 7.9 Dimensionality Reduction
    10. 7.10 Ensemble Learning
    11. 7.11 Markov Chain Monte Carlo
    12. 7.12 Filtering Technique
    13. 7.13 Bayesian Network
    14. 7.14 Summary
  13. Chapter 8: Agile Transmission Techniques (I): Multiple Input Multiple Output
    1. 8.1 Benefits of MIMO
    2. 8.2 Space Time Coding
    3. 8.3 Multi-User MIMO
    4. 8.4 MIMO Network
    5. 8.5 MIMO Cognitive Radio Network
    6. 8.6 Summary
  14. Chapter 9: Agile Transmission Techniques (II): Orthogonal Frequency Division Multiplexing
    1. 9.1 OFDM Implementation
    2. 9.2 Synchronization
    3. 9.3 Channel Estimation
    4. 9.4 Peak Power Problem
    5. 9.5 Adaptive Transmission
    6. 9.6 Spectrum Shaping
    7. 9.7 Orthogonal Frequency Division Multiple Access
    8. 9.8 MIMO OFDM
    9. 9.9 OFDM Cognitive Radio Network
    10. 9.10 Summary
  15. Chapter 10: Game Theory
    1. 10.1 Basic Concepts of Games
    2. 10.2 Primary User Emulation Attack Games
    3. 10.3 Games in Channel Synchronization
    4. 10.4 Games in Collaborative Spectrum Sensing
  16. Chapter 11: Cognitive Radio Network
    1. 11.1 Basic Concepts of Networks
    2. 11.2 Channel Allocation in MAC Layer
    3. 11.3 Scheduling in MAC Layer
    4. 11.4 Routing in Network Layer
    5. 11.5 Congestion Control in Transport Layer
    6. 11.6 Complex Networks in Cognitive Radio
  17. Chapter 12: Cognitive Radio Network as Sensors
    1. 12.1 Intrusion Detection by Machine Learning
    2. 12.2 Joint Spectrum Sensing and Localization
    3. 12.3 Distributed Aspect Synthetic Aperture Radar
    4. 12.4 Wireless Tomography
    5. 12.5 Mobile Crowdsensing
    6. 12.6 Integration of 3S
    7. 12.7 The Cyber-Physical System
    8. 12.8 Computing
    9. 12.9 Security and Privacy
    10. 12.10 Summary
  18. Appendix A: Matrix Analysis
    1. A.1 Vector Spaces and Hilbert Space
    2. A.2 Transformations
    3. A.3 Trace
    4. A.4 Basics of C*-Algebra
    5. A.5 Noncommunicative Matrix-Valued Random Variables
    6. A.6 Distances and Projections
  19. References
  20. Index