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Dynamic Spectrum Management : From Cognitive Radio to Blockchain and Artificial Intelligence.

Bibliographic Details
Main Author: Liang, Ying-Chang.
Format: eBook
Language:English
Published: Singapore : Springer Singapore Pte. Limited, 2019.
Edition:1st ed.
Series:Signals and Communication Technology Series
Subjects:
Electronic books.
Online Access:Click to View
Table of Contents:
  • Intro
  • Preface
  • Acknowledgements
  • Contents
  • Acronyms
  • 1 Introduction
  • 1.1 Background
  • 1.2 Dynamic Spectrum Management
  • 1.2.1 Opportunistic Spectrum Access
  • 1.2.2 Concurrent Spectrum Access
  • 1.3 Cognitive Radio for Dynamic Spectrum Management
  • 1.4 Blockchain for Dynamic Spectrum Management
  • 1.5 Artificial Intelligence for Dynamic Spectrum Management
  • 1.6 Outline of the Book
  • References
  • 2 Opportunistic Spectrum Access
  • 2.1 Introduction
  • 2.2 Sensing-Throughput Tradeoff
  • 2.2.1 Basic Formulation
  • 2.2.2 Cooperative Spectrum Sensing
  • 2.3 Spectrum Sensing Scheduling
  • 2.4 Sequential Spectrum Sensing
  • 2.4.1 Given Sensing Order
  • 2.4.2 Optimal Sensing Order
  • 2.5 Applications: LTE-U
  • 2.5.1 LBT-Based Medium Access Control Protocol Design
  • 2.5.2 User Association: To be WiFi or LTE-U User?
  • 2.6 Summary
  • References
  • 3 Spectrum Sensing Theories and Methods
  • 3.1 Introduction
  • 3.1.1 System Model for Spectrum Sensing
  • 3.1.2 Design Challenges for Spectrum Sensing
  • 3.2 Classical Detection Theories and Methods
  • 3.2.1 Neyman-Pearson Theorem
  • 3.2.2 Bayesian Method and the Generalized Likelihood Ratio Test
  • 3.2.3 Robust Hypothesis Testing
  • 3.2.4 Energy Detection
  • 3.2.5 Sequential Energy Detection
  • 3.2.6 Matched Filtering
  • 3.2.7 Cyclostationary Detection
  • 3.2.8 Detection Threshold and Test Statistic Distribution
  • 3.3 Eigenvalue Based Detections
  • 3.3.1 The Methods
  • 3.3.2 Threshold Setting
  • 3.3.3 Performances of the Methods
  • 3.4 Covariance Based Detections
  • 3.4.1 The Methods
  • 3.4.2 Detection Probability and Threshold Determination
  • 3.4.3 Performance Analysis and Comparison
  • 3.5 Cooperative Spectrum Sensing
  • 3.5.1 Data Fusion
  • 3.5.2 Decision Fusion
  • 3.5.3 Robustness of Cooperative Sensing
  • 3.5.4 Cooperative CBD and EBD
  • 3.6 Summary
  • References.
  • 4 Concurrent Spectrum Access
  • 4.1 Introduction
  • 4.2 Single-Antenna CSA
  • 4.2.1 Power Constraints
  • 4.2.2 Optimal Transmit Power Design
  • 4.3 Cognitive Beamforming
  • 4.3.1 Interference Channel Learning
  • 4.3.2 CB with Perfect Channel Learning
  • 4.3.3 CB with Imperfect Channel Learning: A Learning-Throughput Tradeoff
  • 4.4 Cognitive MIMO
  • 4.4.1 Spatial Spectrum Design
  • 4.4.2 Learning-Based Joint Spatial Spectrum Design
  • 4.5 Cognitive Multiple-Access and Broadcasting Channels
  • 4.5.1 Cognitive Multiple-Access Channel
  • 4.5.2 Cognitive Broadcasting Channel
  • 4.6 Robust Design
  • 4.6.1 Uncertain Interference Channel
  • 4.6.2 Uncertain Interference and Secondary Signal Channels
  • 4.7 Application: Spectrum Refarming
  • 4.7.1 SR with Active Infrastructure Sharing
  • 4.7.2 SR with Passive Infrastructure Sharing
  • 4.7.3 SR in Heterogeneous Networks
  • 4.8 Summary
  • References
  • 5 Blockchain for Dynamic Spectrum Management
  • 5.1 Introduction
  • 5.2 Blockchain Technologies
  • 5.2.1 Overview of Blockchain
  • 5.2.2 Features and the Potential Attacks on Blockchain
  • 5.2.3 Smart Contracts Enabled by Blockchain
  • 5.3 Blockchain for Spectrum Management: Basic Principles
  • 5.3.1 Blockchain as a Secure Database for Spectrum Management
  • 5.3.2 Self-organized Spectrum Market Supported by Blockchain
  • 5.3.3 Deployment of Blockchain over Cognitive Radio Networks
  • 5.3.4 Challenges of Applying Blockchain to Spectrum Management
  • 5.4 Blockchain for Spectrum Management: Examples
  • 5.4.1 Consensus-Based Dynamic Spectrum Access
  • 5.4.2 Secure Spectrum Auctions with Blockchain
  • 5.4.3 Secure Spectrum Sensing Service with Smart Contracts
  • 5.4.4 Blockchain-Enabled Cooperative Dynamic Spectrum Access
  • 5.5 Future Directions
  • 5.6 Summary
  • References
  • 6 Artificial Intelligence for Dynamic Spectrum Management
  • 6.1 Introduction.
  • 6.2 Overview of Machine Learning Techniques
  • 6.2.1 Statistical Machine Learning
  • 6.2.2 Deep Learning
  • 6.2.3 Deep Reinforcement Learning
  • 6.3 Machine Learning for Spectrum Sensing
  • 6.4 Machine Learning for Signal Classification
  • 6.4.1 Modulation-Constrained Clustering Approach
  • 6.4.2 Deep Learning Approach
  • 6.5 Deep Reinforcement Learning for Dynamic Spectrum Access
  • 6.5.1 Deep Multi-user Reinforcement Learning for Distributed Dynamic Spectrum Access
  • 6.5.2 Deep Reinforcement Learning for Joint User Association and Resource Allocation
  • 6.6 Summary
  • References.

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