Data-Driven Wireless Networks
A Compressive Spectrum Approach
1st Edition
Published on 19. October 2018
XIX, 93 pages
978-3-030-00290-9 (ISBN)
Description
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.
More details
Series
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Primary & secondary/elementary & high school
Product notice
Reflowable
Illustrations
XIX, 93 p. 35 illus. in color.
File size
3,17 MB
ISBN-13
978-3-030-00290-9 (9783030002909)
DOI
10.1007/978-3-030-00290-9
Schweitzer Classification
Other editions
Additional editions
Book
11/2018
Springer
€53.49
Shipment within 7-9 days
Content
1 - Foreword [Seite 7]
2 - Preface [Seite 8]
3 - Acknowledgment [Seite 9]
4 - Contents [Seite 10]
5 - Acronyms and Nomenclature [Seite 13]
6 - Part I Background [Seite 16]
6.1 - 1 Introduction [Seite 17]
6.1.1 - 1.1 Motivations and Contributions [Seite 18]
6.1.1.1 - 1.1.1 Data-Driven Compressive Spectrum Sensing [Seite 19]
6.1.1.2 - 1.1.2 Robust Compressive Spectrum Sensing [Seite 19]
6.1.1.3 - 1.1.3 Secure Compressive Spectrum Sensing [Seite 20]
6.1.2 - References [Seite 21]
6.2 - 2 Sparse Representation in Wireless Networks [Seite 23]
6.2.1 - 2.1 Principles of Standard Compressive Sensing [Seite 23]
6.2.1.1 - 2.1.1 Sparse Representation [Seite 24]
6.2.1.2 - 2.1.2 Projection [Seite 24]
6.2.1.3 - 2.1.3 Signal Reconstruction [Seite 26]
6.2.2 - 2.2 Reweighted Compressive Sensing [Seite 27]
6.2.3 - 2.3 Distributed Compressive Sensing [Seite 28]
6.2.4 - 2.4 Compressive Spectrum Sensing [Seite 29]
6.2.4.1 - 2.4.1 Spectrum Sensing Methods [Seite 29]
6.2.4.2 - 2.4.2 Spectrum Sensing Model [Seite 30]
6.2.4.3 - 2.4.3 Compressive Wideband Spectrum Sensing [Seite 31]
6.2.4.3.1 - 2.4.3.1 Signals Arrives at Secondary Users [Seite 32]
6.2.4.3.2 - 2.4.3.2 Compressed Measurements Collection [Seite 32]
6.2.4.3.3 - 2.4.3.3 Signal Recovery [Seite 32]
6.2.4.3.4 - 2.4.3.4 Decision Making [Seite 33]
6.2.5 - 2.5 Summary [Seite 33]
6.2.6 - References [Seite 33]
7 - Part II Compressive Spectrum Sensing Algorithms [Seite 35]
7.1 - 3 Data-Driven Compressive Spectrum Sensing [Seite 36]
7.1.1 - 3.1 Introduction [Seite 36]
7.1.1.1 - 3.1.1 Related Work [Seite 37]
7.1.1.2 - 3.1.2 Contributions [Seite 38]
7.1.2 - 3.2 Data-Driven Compressive Spectrum Sensing Framework [Seite 38]
7.1.2.1 - 3.2.1 Iteratively Reweighted Least Square-Based Compressive Sensing [Seite 39]
7.1.2.2 - 3.2.2 Non-iteratively Reweighted Least Square-Based Compressive Sensing [Seite 41]
7.1.2.2.1 - 3.2.2.1 Convergence Analyses [Seite 42]
7.1.2.2.2 - 3.2.2.2 Complexity Analyses [Seite 43]
7.1.2.3 - 3.2.3 Proposed Wilkinson's Method-Based DTT Location Probability Calculation Algorithm [Seite 44]
7.1.2.3.1 - 3.2.3.1 Maximum Allowable Equivalent Isotropic Radiated Power Calculation [Seite 44]
7.1.3 - 3.3 Numerical Analyses [Seite 46]
7.1.3.1 - 3.3.1 Numerical Analyses on Simulated Signals and Data [Seite 46]
7.1.3.2 - 3.3.2 Numerical Analyses on Real-World Signals and Data [Seite 51]
7.1.4 - 3.4 Summary [Seite 52]
7.1.5 - References [Seite 53]
7.2 - 4 Robust Compressive Spectrum Sensing [Seite 55]
7.2.1 - 4.1 Introduction [Seite 55]
7.2.1.1 - 4.1.1 Related Work [Seite 55]
7.2.1.2 - 4.1.2 Contributions [Seite 56]
7.2.2 - 4.2 Robust Compressive Spectrum Sensing at Single User [Seite 57]
7.2.2.1 - 4.2.1 System Model [Seite 57]
7.2.2.1.1 - 4.2.1.1 Proposed Channel Division Scheme [Seite 57]
7.2.2.1.2 - 4.2.1.2 Proposed Denoised Spectrum Sensing Algorithm [Seite 58]
7.2.2.2 - 4.2.2 Computational Complexity and Spectrum Usage Analyses [Seite 59]
7.2.3 - 4.3 Numerical Analyses for Single User Case [Seite 61]
7.2.3.1 - 4.3.1 Analyses on Simulated Signals [Seite 61]
7.2.3.2 - 4.3.2 Analyses on Real-World Signals [Seite 64]
7.2.4 - 4.4 Matrix Completion-Based Robust Spectrum Sensing at Cooperative Multiple Users [Seite 65]
7.2.4.1 - 4.4.1 System Model [Seite 66]
7.2.4.1.1 - 4.4.1.1 Signals Arrive at Secondary Users [Seite 67]
7.2.4.1.2 - 4.4.1.2 Incomplete Matrix Construction at Fusion Center [Seite 68]
7.2.4.1.3 - 4.4.1.3 Matrix Completion at Fusion Center [Seite 68]
7.2.4.1.4 - 4.4.1.4 Decision Making at an Fusion Center [Seite 69]
7.2.4.2 - 4.4.2 Denoised Cooperative Spectrum Sensing Algorithm [Seite 69]
7.2.4.3 - 4.4.3 Computational Complexity and Performance Analyses [Seite 70]
7.2.5 - 4.5 Numerical Analyses for Cooperative Multiple Users Case [Seite 70]
7.2.5.1 - 4.5.1 Analyses on Simulated Signals [Seite 70]
7.2.5.2 - 4.5.2 Analyses on Real-World Signals [Seite 73]
7.2.6 - 4.6 Summary [Seite 74]
7.2.7 - References [Seite 75]
7.3 - 5 Secure Compressive Spectrum Sensing [Seite 77]
7.3.1 - 5.1 Introduction [Seite 77]
7.3.1.1 - 5.1.1 Related Work [Seite 78]
7.3.1.2 - 5.1.2 Motivations and Contributions [Seite 79]
7.3.2 - 5.2 System Model [Seite 80]
7.3.2.1 - 5.2.1 Networks Description [Seite 80]
7.3.2.2 - 5.2.2 Signal Processing Model [Seite 82]
7.3.3 - 5.3 Malicious User Detection Framework [Seite 83]
7.3.3.1 - 5.3.1 Proposed Malicious User Detection Algorithm [Seite 84]
7.3.3.2 - 5.3.2 Rank Order Estimation Algorithm [Seite 87]
7.3.3.3 - 5.3.3 Malicious User Number Estimation [Seite 90]
7.3.3.4 - 5.3.4 Analyses on Minimal Number of Active Secondary Users [Seite 91]
7.3.4 - 5.4 Numerical Analyses [Seite 92]
7.3.4.1 - 5.4.1 Numerical Results Using Simulated Signals [Seite 93]
7.3.4.1.1 - 5.4.1.1 Results of the Proposed Rank Order Estimation [Seite 93]
7.3.4.1.2 - 5.4.1.2 Results of the Case with Unknown Number of Malicious Users [Seite 93]
7.3.4.1.3 - 5.4.1.3 Results of the Proposed Malicious User Detection [Seite 94]
7.3.4.2 - 5.4.2 Numerical Results Using Real-World Signals [Seite 97]
7.3.5 - 5.5 Summary [Seite 98]
7.3.6 - References [Seite 99]
8 - Part III Conclusions [Seite 101]
8.1 - 6 Conclusions and Future Work [Seite 102]
8.1.1 - 6.1 Conclusions [Seite 102]
8.1.2 - 6.2 Future Work [Seite 103]
8.1.3 - References [Seite 104]
2 - Preface [Seite 8]
3 - Acknowledgment [Seite 9]
4 - Contents [Seite 10]
5 - Acronyms and Nomenclature [Seite 13]
6 - Part I Background [Seite 16]
6.1 - 1 Introduction [Seite 17]
6.1.1 - 1.1 Motivations and Contributions [Seite 18]
6.1.1.1 - 1.1.1 Data-Driven Compressive Spectrum Sensing [Seite 19]
6.1.1.2 - 1.1.2 Robust Compressive Spectrum Sensing [Seite 19]
6.1.1.3 - 1.1.3 Secure Compressive Spectrum Sensing [Seite 20]
6.1.2 - References [Seite 21]
6.2 - 2 Sparse Representation in Wireless Networks [Seite 23]
6.2.1 - 2.1 Principles of Standard Compressive Sensing [Seite 23]
6.2.1.1 - 2.1.1 Sparse Representation [Seite 24]
6.2.1.2 - 2.1.2 Projection [Seite 24]
6.2.1.3 - 2.1.3 Signal Reconstruction [Seite 26]
6.2.2 - 2.2 Reweighted Compressive Sensing [Seite 27]
6.2.3 - 2.3 Distributed Compressive Sensing [Seite 28]
6.2.4 - 2.4 Compressive Spectrum Sensing [Seite 29]
6.2.4.1 - 2.4.1 Spectrum Sensing Methods [Seite 29]
6.2.4.2 - 2.4.2 Spectrum Sensing Model [Seite 30]
6.2.4.3 - 2.4.3 Compressive Wideband Spectrum Sensing [Seite 31]
6.2.4.3.1 - 2.4.3.1 Signals Arrives at Secondary Users [Seite 32]
6.2.4.3.2 - 2.4.3.2 Compressed Measurements Collection [Seite 32]
6.2.4.3.3 - 2.4.3.3 Signal Recovery [Seite 32]
6.2.4.3.4 - 2.4.3.4 Decision Making [Seite 33]
6.2.5 - 2.5 Summary [Seite 33]
6.2.6 - References [Seite 33]
7 - Part II Compressive Spectrum Sensing Algorithms [Seite 35]
7.1 - 3 Data-Driven Compressive Spectrum Sensing [Seite 36]
7.1.1 - 3.1 Introduction [Seite 36]
7.1.1.1 - 3.1.1 Related Work [Seite 37]
7.1.1.2 - 3.1.2 Contributions [Seite 38]
7.1.2 - 3.2 Data-Driven Compressive Spectrum Sensing Framework [Seite 38]
7.1.2.1 - 3.2.1 Iteratively Reweighted Least Square-Based Compressive Sensing [Seite 39]
7.1.2.2 - 3.2.2 Non-iteratively Reweighted Least Square-Based Compressive Sensing [Seite 41]
7.1.2.2.1 - 3.2.2.1 Convergence Analyses [Seite 42]
7.1.2.2.2 - 3.2.2.2 Complexity Analyses [Seite 43]
7.1.2.3 - 3.2.3 Proposed Wilkinson's Method-Based DTT Location Probability Calculation Algorithm [Seite 44]
7.1.2.3.1 - 3.2.3.1 Maximum Allowable Equivalent Isotropic Radiated Power Calculation [Seite 44]
7.1.3 - 3.3 Numerical Analyses [Seite 46]
7.1.3.1 - 3.3.1 Numerical Analyses on Simulated Signals and Data [Seite 46]
7.1.3.2 - 3.3.2 Numerical Analyses on Real-World Signals and Data [Seite 51]
7.1.4 - 3.4 Summary [Seite 52]
7.1.5 - References [Seite 53]
7.2 - 4 Robust Compressive Spectrum Sensing [Seite 55]
7.2.1 - 4.1 Introduction [Seite 55]
7.2.1.1 - 4.1.1 Related Work [Seite 55]
7.2.1.2 - 4.1.2 Contributions [Seite 56]
7.2.2 - 4.2 Robust Compressive Spectrum Sensing at Single User [Seite 57]
7.2.2.1 - 4.2.1 System Model [Seite 57]
7.2.2.1.1 - 4.2.1.1 Proposed Channel Division Scheme [Seite 57]
7.2.2.1.2 - 4.2.1.2 Proposed Denoised Spectrum Sensing Algorithm [Seite 58]
7.2.2.2 - 4.2.2 Computational Complexity and Spectrum Usage Analyses [Seite 59]
7.2.3 - 4.3 Numerical Analyses for Single User Case [Seite 61]
7.2.3.1 - 4.3.1 Analyses on Simulated Signals [Seite 61]
7.2.3.2 - 4.3.2 Analyses on Real-World Signals [Seite 64]
7.2.4 - 4.4 Matrix Completion-Based Robust Spectrum Sensing at Cooperative Multiple Users [Seite 65]
7.2.4.1 - 4.4.1 System Model [Seite 66]
7.2.4.1.1 - 4.4.1.1 Signals Arrive at Secondary Users [Seite 67]
7.2.4.1.2 - 4.4.1.2 Incomplete Matrix Construction at Fusion Center [Seite 68]
7.2.4.1.3 - 4.4.1.3 Matrix Completion at Fusion Center [Seite 68]
7.2.4.1.4 - 4.4.1.4 Decision Making at an Fusion Center [Seite 69]
7.2.4.2 - 4.4.2 Denoised Cooperative Spectrum Sensing Algorithm [Seite 69]
7.2.4.3 - 4.4.3 Computational Complexity and Performance Analyses [Seite 70]
7.2.5 - 4.5 Numerical Analyses for Cooperative Multiple Users Case [Seite 70]
7.2.5.1 - 4.5.1 Analyses on Simulated Signals [Seite 70]
7.2.5.2 - 4.5.2 Analyses on Real-World Signals [Seite 73]
7.2.6 - 4.6 Summary [Seite 74]
7.2.7 - References [Seite 75]
7.3 - 5 Secure Compressive Spectrum Sensing [Seite 77]
7.3.1 - 5.1 Introduction [Seite 77]
7.3.1.1 - 5.1.1 Related Work [Seite 78]
7.3.1.2 - 5.1.2 Motivations and Contributions [Seite 79]
7.3.2 - 5.2 System Model [Seite 80]
7.3.2.1 - 5.2.1 Networks Description [Seite 80]
7.3.2.2 - 5.2.2 Signal Processing Model [Seite 82]
7.3.3 - 5.3 Malicious User Detection Framework [Seite 83]
7.3.3.1 - 5.3.1 Proposed Malicious User Detection Algorithm [Seite 84]
7.3.3.2 - 5.3.2 Rank Order Estimation Algorithm [Seite 87]
7.3.3.3 - 5.3.3 Malicious User Number Estimation [Seite 90]
7.3.3.4 - 5.3.4 Analyses on Minimal Number of Active Secondary Users [Seite 91]
7.3.4 - 5.4 Numerical Analyses [Seite 92]
7.3.4.1 - 5.4.1 Numerical Results Using Simulated Signals [Seite 93]
7.3.4.1.1 - 5.4.1.1 Results of the Proposed Rank Order Estimation [Seite 93]
7.3.4.1.2 - 5.4.1.2 Results of the Case with Unknown Number of Malicious Users [Seite 93]
7.3.4.1.3 - 5.4.1.3 Results of the Proposed Malicious User Detection [Seite 94]
7.3.4.2 - 5.4.2 Numerical Results Using Real-World Signals [Seite 97]
7.3.5 - 5.5 Summary [Seite 98]
7.3.6 - References [Seite 99]
8 - Part III Conclusions [Seite 101]
8.1 - 6 Conclusions and Future Work [Seite 102]
8.1.1 - 6.1 Conclusions [Seite 102]
8.1.2 - 6.2 Future Work [Seite 103]
8.1.3 - References [Seite 104]
