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