Data-Driven Wireless Networks (eBook)

A Compressive Spectrum Approach

Yue Gao, Zhijin Qin (Autoren)

eBook Download: PDF

2018
XIX, 93 Seiten
Springer International Publishing (Verlag)
978-3-030-00290-9 (ISBN)

Infos

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.

Inhalt

Foreword 7
Preface 8
Acknowledgment 9
Contents 10
Acronyms and Nomenclature 13
Part I Background 16
1 Introduction 17
1.1 Motivations and Contributions 18
1.1.1 Data-Driven Compressive Spectrum Sensing 19
1.1.2 Robust Compressive Spectrum Sensing 19
1.1.3 Secure Compressive Spectrum Sensing 20
References 21
2 Sparse Representation in Wireless Networks 23
2.1 Principles of Standard Compressive Sensing 23
2.1.1 Sparse Representation 24
2.1.2 Projection 24
2.1.3 Signal Reconstruction 26
2.2 Reweighted Compressive Sensing 27
2.3 Distributed Compressive Sensing 28
2.4 Compressive Spectrum Sensing 29
2.4.1 Spectrum Sensing Methods 29
2.4.2 Spectrum Sensing Model 30
2.4.3 Compressive Wideband Spectrum Sensing 31
2.4.3.1 Signals Arrives at Secondary Users 32
2.4.3.2 Compressed Measurements Collection 32
2.4.3.3 Signal Recovery 32
2.4.3.4 Decision Making 33
2.5 Summary 33
References 33
Part II Compressive Spectrum Sensing Algorithms 35
3 Data-Driven Compressive Spectrum Sensing 36
3.1 Introduction 36
3.1.1 Related Work 37
3.1.2 Contributions 38
3.2 Data-Driven Compressive Spectrum Sensing Framework 38
3.2.1 Iteratively Reweighted Least Square-Based Compressive Sensing 39
3.2.2 Non-iteratively Reweighted Least Square-Based Compressive Sensing 41
3.2.2.1 Convergence Analyses 42
3.2.2.2 Complexity Analyses 43
3.2.3 Proposed Wilkinson's Method-Based DTT Location Probability Calculation Algorithm 44
3.2.3.1 Maximum Allowable Equivalent Isotropic Radiated Power Calculation 44
3.3 Numerical Analyses 46
3.3.1 Numerical Analyses on Simulated Signals and Data 46
3.3.2 Numerical Analyses on Real-World Signals and Data 51
3.4 Summary 52
References 53
4 Robust Compressive Spectrum Sensing 55
4.1 Introduction 55
4.1.1 Related Work 55
4.1.2 Contributions 56
4.2 Robust Compressive Spectrum Sensing at Single User 57
4.2.1 System Model 57
4.2.1.1 Proposed Channel Division Scheme 57
4.2.1.2 Proposed Denoised Spectrum Sensing Algorithm 58
4.2.2 Computational Complexity and Spectrum Usage Analyses 59
4.3 Numerical Analyses for Single User Case 61
4.3.1 Analyses on Simulated Signals 61
4.3.2 Analyses on Real-World Signals 64
4.4 Matrix Completion-Based Robust Spectrum Sensing at Cooperative Multiple Users 65
4.4.1 System Model 66
4.4.1.1 Signals Arrive at Secondary Users 67
4.4.1.2 Incomplete Matrix Construction at Fusion Center 68
4.4.1.3 Matrix Completion at Fusion Center 68
4.4.1.4 Decision Making at an Fusion Center 69
4.4.2 Denoised Cooperative Spectrum Sensing Algorithm 69
4.4.3 Computational Complexity and Performance Analyses 70
4.5 Numerical Analyses for Cooperative Multiple Users Case 70
4.5.1 Analyses on Simulated Signals 70
4.5.2 Analyses on Real-World Signals 73
4.6 Summary 74
References 75
5 Secure Compressive Spectrum Sensing 77
5.1 Introduction 77
5.1.1 Related Work 78
5.1.2 Motivations and Contributions 79
5.2 System Model 80
5.2.1 Networks Description 80
5.2.2 Signal Processing Model 82
5.3 Malicious User Detection Framework 83
5.3.1 Proposed Malicious User Detection Algorithm 84
5.3.2 Rank Order Estimation Algorithm 87
5.3.3 Malicious User Number Estimation 90
5.3.4 Analyses on Minimal Number of Active Secondary Users 91
5.4 Numerical Analyses 92
5.4.1 Numerical Results Using Simulated Signals 93
5.4.1.1 Results of the Proposed Rank Order Estimation 93
5.4.1.2 Results of the Case with Unknown Number of Malicious Users 93
5.4.1.3 Results of the Proposed Malicious User Detection 94
5.4.2 Numerical Results Using Real-World Signals 97
5.5 Summary 98
References 99
Part III Conclusions 101
6 Conclusions and Future Work 102
6.1 Conclusions 102
6.2 Future Work 103
References 104

Produkt-Details

• Erscheint lt. Verlag: 19.10.2018
• Reihe/Serie: SpringerBriefs in Electrical and Computer Engineering
• Reihe/Serie: SpringerBriefs in Electrical and Computer Engineering
• Zusatzinfo: XIX, 93 p. 35 illus. in color.
• Verlagsort: Cham
• Sprache: englisch
• Themenwelt: Technik ► Elektrotechnik / Energietechnik
• Schlagworte: Cognitive Radio • Cognitive Radio Networks • compressive sensing • compressive spectrum sensing • data analytics • Data-driven • radio spectrum • spectrum database • sub-Nyquist sampling • TV White Space • widebrand spectrum • wireless communications
• ISBN-10: 3-030-00290-X / 303000290X
• ISBN-13: 978-3-030-00290-9 / 9783030002909