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

By: Liang, Ying-ChangMaterial type: TextTextSeries: Signals and Communication Technology SeriesPublisher: Singapore : Springer Singapore Pte. Limited, 2019Copyright date: �2020Edition: 1st edDescription: 1 online resource (180 pages)Content type: text Media type: computer Carrier type: online resourceISBN: 9789811507762Genre/Form: Electronic books.Additional physical formats: Print version:: Dynamic Spectrum ManagementLOC classification: TK5101-5105.9Online resources: Click to View
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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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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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2023. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

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