Dedication
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
- 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
- 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
- Chapter 9: Agile Transmission Techniques (II): Orthogonal Frequency Division Multiplexing
- Chapter 10: Game Theory
- Chapter 11: Cognitive Radio Network
- Chapter 12: Cognitive Radio Network as Sensors
- Appendix A: Matrix Analysis
- References
- Index
To my family Lily Liman Li, Michelle, David and Jackie
—Robert C. Qiu
To my wife, Qingying Meng
—Zhen Hu
To my wife Min Duan and my son Siyi
—Husheng Li
Our families
—Michael C. Wicks
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