book

Cognitive Radio Communication and Networking: Principles and Practice

by Husheng Li, Zhen Hu, Michael C. Wicks, Robert Caiming Qiu

November 2012

Beginner

534 pages

17h 2m

English

Wiley

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Cover
Title Page
Copyright
Dedication
Preface
Chapter 1: Introduction
1.1 Vision: “Big Data”1.2 Cognitive Radio: System Concepts1.3 Spectrum Sensing Interface and Data Structures1.4 Mathematical Machinery1.5 Sample Covariance Matrix1.6 Large Sample Covariance Matrices of Spiked Population Models1.7 Random Matrices and Noncommutative Random Variables1.8 Principal Component Analysis1.9 Generalized Likelihood Ratio Test (GLRT)1.10 Bregman Divergence for Matrix Nearness
Chapter 2: Spectrum Sensing: Basic Techniques
2.1 Challenges2.2 Energy Detection: No Prior Information about Deterministic or Stochastic Signal2.3 Spectrum Sensing Exploiting Second-Order Statistics2.4 Statistical Pattern Recognition: Exploiting Prior Information about Signal through Machine Learning2.5 Feature Template Matching2.6 Cyclostationary Detection
Chapter 3: Classical Detection
3.1 Formalism of Quantum Information3.2 Hypothesis Detection for Collaborative Sensing3.3 Sample Covariance Matrix3.4 Random Matrices with Independent Rows3.5 The Multivariate Normal Distribution3.6 Sample Covariance Matrix Estimation and Matrix Compressed Sensing3.7 Likelihood Ratio Test
Chapter 4: Hypothesis Detection of Noncommutative Random Matrices
4.1 Why Noncommutative Random Matrices?4.2 Partial Orders of Covariance Matrices: A < B4.3 Partial Ordering of Completely Positive Mappings: Φ(A) < Φ(B)4.4 Partial Ordering of Matrices Using Majorization: A < B4.5 Partial Ordering of Unitarily Invariant Norms: |||A||| < |||B|||4.6 Partial Ordering of Positive Definite Matrices of Many Copies:4.7 Partial Ordering of Positive Operator Valued Random Variables: Prob(A ≤ X ≤ B)4.8 Partial Ordering Using Stochastic Order: A ≤ stB4.9 Quantum Hypothesis Detection4.10 Quantum Hypothesis Testing for Many Copies
Chapter 5: Large Random Matrices
5.1 Large Dimensional Random Matrices: Moment Approach, Stieltjes Transform and Free Probability5.2 Spectrum Sensing Using Large Random Matrices5.3 Moment Approach5.4 Stieltjes Transform

5.5 Case Studies and Applications
5.6 Regularized Estimation of Large Covariance Matrices5.7 Free Probability
Chapter 6: Convex Optimization
6.1 Linear Programming6.2 Quadratic Programming6.3 Semidefinite Programming6.4 Geometric Programming6.5 Lagrange Duality6.6 Optimization Algorithm6.7 Robust Optimization6.8 Multiobjective Optimization6.9 Optimization for Radio Resource Management6.10 Examples and Applications6.11 Summary
Chapter 7: Machine Learning
7.1 Unsupervised Learning7.2 Supervised Learning7.3 Semisupervised Learning7.4 Transductive Inference7.5 Transfer Learning7.6 Active Learning7.7 Reinforcement Learning7.8 Kernel-Based Learning7.9 Dimensionality Reduction7.10 Ensemble Learning7.11 Markov Chain Monte Carlo7.12 Filtering Technique7.13 Bayesian Network7.14 Summary
Chapter 8: Agile Transmission Techniques (I): Multiple Input Multiple Output
8.1 Benefits of MIMO8.2 Space Time Coding8.3 Multi-User MIMO8.4 MIMO Network8.5 MIMO Cognitive Radio Network8.6 Summary
Chapter 9: Agile Transmission Techniques (II): Orthogonal Frequency Division Multiplexing
9.1 OFDM Implementation9.2 Synchronization9.3 Channel Estimation9.4 Peak Power Problem9.5 Adaptive Transmission9.6 Spectrum Shaping9.7 Orthogonal Frequency Division Multiple Access9.8 MIMO OFDM9.9 OFDM Cognitive Radio Network9.10 Summary
Chapter 10: Game Theory
10.1 Basic Concepts of Games10.2 Primary User Emulation Attack Games10.3 Games in Channel Synchronization10.4 Games in Collaborative Spectrum Sensing
Chapter 11: Cognitive Radio Network
11.1 Basic Concepts of Networks11.2 Channel Allocation in MAC Layer11.3 Scheduling in MAC Layer11.4 Routing in Network Layer11.5 Congestion Control in Transport Layer11.6 Complex Networks in Cognitive Radio
Chapter 12: Cognitive Radio Network as Sensors
12.1 Intrusion Detection by Machine Learning12.2 Joint Spectrum Sensing and Localization12.3 Distributed Aspect Synthetic Aperture Radar12.4 Wireless Tomography12.5 Mobile Crowdsensing12.6 Integration of 3S12.7 The Cyber-Physical System12.8 Computing12.9 Security and Privacy12.10 Summary
Appendix A: Matrix Analysis
A.1 Vector Spaces and Hilbert SpaceA.2 TransformationsA.3 TraceA.4 Basics of C*-AlgebraA.5 Noncommunicative Matrix-Valued Random VariablesA.6 Distances and Projections
References
Index

Overview

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.

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