Gao / Qin Data-Driven Wireless Networks

A Compressive Spectrum Approach
1. Auflage 2018
ISBN: 978-3-030-00290-9
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 104 Seiten, eBook

Reihe: SpringerBriefs in Electrical and Computer Engineering

This SpringerBrief discusses the applications of spare representation in wireless communications, with a particular focus on the most recent developed compressive sensing (CS) enabled approaches. With the help of sparsity property, sub-Nyquist sampling can be achieved in wideband cognitive radio networks by adopting compressive sensing, which is illustrated in this brief, and it starts with a comprehensive overview of compressive sensing principles. Subsequently, the authors present a complete framework for data-driven compressive spectrum sensing in cognitive radio networks, which guarantees robustness, low-complexity, and security.  Particularly, robust compressive spectrum sensing, low-complexity compressive spectrum sensing, and secure compressive sensing based malicious user detection are proposed to address the various issues in wideband cognitive radio networks. Correspondingly, the real-world signals and data collected by experiments carried out during TV white space pilot trial enables data-driven compressive spectrum sensing. The collected data are analysed and used to verify our designs and provide significant insights on the potential of applying compressive sensing to wideband spectrum sensing.  This SpringerBrief  provides readers a clear picture on how to exploit the compressive sensing to process wireless signals in wideband cognitive radio networks.  Students, professors, researchers, scientists, practitioners, and engineers working in the fields of compressive sensing in wireless communications will find this SpringerBrief  very useful as a short reference or study guide book.  Industry managers, and government research agency employees also working in the fields of compressive sensing in wireless communications will find this SpringerBrief useful as well.

Zielgruppe

Graduate

Autoren/Hrsg.

Gao, Yue

Qin, Zhijin

Inhaltsverzeichnis

1;Foreword;7
2;Preface;8
3;Acknowledgment;9
4;Contents;10
5;Acronyms and Nomenclature;13
6;Part I Background;16
6.1;1 Introduction;17
6.1.1;1.1 Motivations and Contributions;18
6.1.1.1;1.1.1 Data-Driven Compressive Spectrum Sensing;19
6.1.1.2;1.1.2 Robust Compressive Spectrum Sensing;19
6.1.1.3;1.1.3 Secure Compressive Spectrum Sensing;20
6.1.2;References;21
6.2;2 Sparse Representation in Wireless Networks;23
6.2.1;2.1 Principles of Standard Compressive Sensing;23
6.2.1.1;2.1.1 Sparse Representation;24
6.2.1.2;2.1.2 Projection;24
6.2.1.3;2.1.3 Signal Reconstruction;26
6.2.2;2.2 Reweighted Compressive Sensing;27
6.2.3;2.3 Distributed Compressive Sensing;28
6.2.4;2.4 Compressive Spectrum Sensing;29
6.2.4.1;2.4.1 Spectrum Sensing Methods;29
6.2.4.2;2.4.2 Spectrum Sensing Model;30
6.2.4.3;2.4.3 Compressive Wideband Spectrum Sensing;31
6.2.4.3.1;2.4.3.1 Signals Arrives at Secondary Users;32
6.2.4.3.2;2.4.3.2 Compressed Measurements Collection;32
6.2.4.3.3;2.4.3.3 Signal Recovery;32
6.2.4.3.4;2.4.3.4 Decision Making;33
6.2.5;2.5 Summary;33
6.2.6;References;33
7;Part II Compressive Spectrum Sensing Algorithms;35
7.1;3 Data-Driven Compressive Spectrum Sensing;36
7.1.1;3.1 Introduction;36
7.1.1.1;3.1.1 Related Work;37
7.1.1.2;3.1.2 Contributions;38
7.1.2;3.2 Data-Driven Compressive Spectrum Sensing Framework;38
7.1.2.1;3.2.1 Iteratively Reweighted Least Square-Based Compressive Sensing;39
7.1.2.2;3.2.2 Non-iteratively Reweighted Least Square-Based Compressive Sensing;41
7.1.2.2.1;3.2.2.1 Convergence Analyses;42
7.1.2.2.2;3.2.2.2 Complexity Analyses;43
7.1.2.3;3.2.3 Proposed Wilkinson's Method-Based DTT Location Probability Calculation Algorithm;44
7.1.2.3.1;3.2.3.1 Maximum Allowable Equivalent Isotropic Radiated Power Calculation;44
7.1.3;3.3 Numerical Analyses;46
7.1.3.1;3.3.1 Numerical Analyses on Simulated Signals and Data;46
7.1.3.2;3.3.2 Numerical Analyses on Real-World Signals and Data;51
7.1.4;3.4 Summary;52
7.1.5;References;53
7.2;4 Robust Compressive Spectrum Sensing;55
7.2.1;4.1 Introduction;55
7.2.1.1;4.1.1 Related Work;55
7.2.1.2;4.1.2 Contributions;56
7.2.2;4.2 Robust Compressive Spectrum Sensing at Single User;57
7.2.2.1;4.2.1 System Model;57
7.2.2.1.1;4.2.1.1 Proposed Channel Division Scheme;57
7.2.2.1.2;4.2.1.2 Proposed Denoised Spectrum Sensing Algorithm;58
7.2.2.2;4.2.2 Computational Complexity and Spectrum Usage Analyses;59
7.2.3;4.3 Numerical Analyses for Single User Case;61
7.2.3.1;4.3.1 Analyses on Simulated Signals;61
7.2.3.2;4.3.2 Analyses on Real-World Signals;64
7.2.4;4.4 Matrix Completion-Based Robust Spectrum Sensing at Cooperative Multiple Users;65
7.2.4.1;4.4.1 System Model;66
7.2.4.1.1;4.4.1.1 Signals Arrive at Secondary Users;67
7.2.4.1.2;4.4.1.2 Incomplete Matrix Construction at Fusion Center;68
7.2.4.1.3;4.4.1.3 Matrix Completion at Fusion Center;68
7.2.4.1.4;4.4.1.4 Decision Making at an Fusion Center;69
7.2.4.2;4.4.2 Denoised Cooperative Spectrum Sensing Algorithm;69
7.2.4.3;4.4.3 Computational Complexity and Performance Analyses;70
7.2.5;4.5 Numerical Analyses for Cooperative Multiple Users Case;70
7.2.5.1;4.5.1 Analyses on Simulated Signals;70
7.2.5.2;4.5.2 Analyses on Real-World Signals;73
7.2.6;4.6 Summary;74
7.2.7;References;75
7.3;5 Secure Compressive Spectrum Sensing;77
7.3.1;5.1 Introduction;77
7.3.1.1;5.1.1 Related Work;78
7.3.1.2;5.1.2 Motivations and Contributions;79
7.3.2;5.2 System Model;80
7.3.2.1;5.2.1 Networks Description;80
7.3.2.2;5.2.2 Signal Processing Model;82
7.3.3;5.3 Malicious User Detection Framework;83
7.3.3.1;5.3.1 Proposed Malicious User Detection Algorithm;84
7.3.3.2;5.3.2 Rank Order Estimation Algorithm;87
7.3.3.3;5.3.3 Malicious User Number Estimation;90
7.3.3.4;5.3.4 Analyses on Minimal Number of Active Secondary Users;91
7.3.4;5.4 Numerical Analyses;92
7.3.4.1;5.4.1 Numerical Results Using Simulated Signals;93
7.3.4.1.1;5.4.1.1 Results of the Proposed Rank Order Estimation;93
7.3.4.1.2;5.4.1.2 Results of the Case with Unknown Number of Malicious Users;93
7.3.4.1.3;5.4.1.3 Results of the Proposed Malicious User Detection;94
7.3.4.2;5.4.2 Numerical Results Using Real-World Signals;97
7.3.5;5.5 Summary;98
7.3.6;References;99
8;Part III Conclusions;101
8.1;6 Conclusions and Future Work;102
8.1.1;6.1 Conclusions;102
8.1.2;6.2 Future Work;103
8.1.3;References;104