Data-driven wireless networks : a compressive spectrum approach /: a compressive spectrum approach. (2018)
- Record Type:
- Book
- Title:
- Data-driven wireless networks : a compressive spectrum approach /: a compressive spectrum approach. (2018)
- Main Title:
- Data-driven wireless networks : a compressive spectrum approach
- Further Information:
- Note: Yue Gao, Zhijin Qin.
- Authors:
- Gao, Yue
Qin, Zhijin - Contents:
- Intro; Foreword; Preface; Acknowledgment; Contents; Acronyms and Nomenclature; Part I Background; 1 Introduction; 1.1 Motivations and Contributions; 1.1.1 Data-Driven Compressive Spectrum Sensing; 1.1.2 Robust Compressive Spectrum Sensing; 1.1.3 Secure Compressive Spectrum Sensing; References; 2 Sparse Representation in Wireless Networks; 2.1 Principles of Standard Compressive Sensing; 2.1.1 Sparse Representation; 2.1.2 Projection; 2.1.3 Signal Reconstruction; 2.2 Reweighted Compressive Sensing; 2.3 Distributed Compressive Sensing; 2.4 Compressive Spectrum Sensing 2.4.1 Spectrum Sensing Methods2.4.2 Spectrum Sensing Model; 2.4.3 Compressive Wideband Spectrum Sensing; 2.4.3.1 Signals Arrives at Secondary Users; 2.4.3.2 Compressed Measurements Collection; 2.4.3.3 Signal Recovery; 2.4.3.4 Decision Making; 2.5 Summary; References; Part II Compressive Spectrum Sensing Algorithms; 3 Data-Driven Compressive Spectrum Sensing; 3.1 Introduction; 3.1.1 Related Work; 3.1.2 Contributions; 3.2 Data-Driven Compressive Spectrum Sensing Framework; 3.2.1 Iteratively Reweighted Least Square-Based Compressive Sensing 3.2.2 Non-iteratively Reweighted Least Square-Based Compressive Sensing3.2.2.1 Convergence Analyses; 3.2.2.2 Complexity Analyses; 3.2.3 Proposed Wilkinson's Method-Based DTT Location Probability Calculation Algorithm; 3.2.3.1 Maximum Allowable Equivalent Isotropic Radiated Power Calculation; 3.3 Numerical Analyses; 3.3.1 Numerical Analyses on Simulated Signals and Data; 3.3.2Intro; Foreword; Preface; Acknowledgment; Contents; Acronyms and Nomenclature; Part I Background; 1 Introduction; 1.1 Motivations and Contributions; 1.1.1 Data-Driven Compressive Spectrum Sensing; 1.1.2 Robust Compressive Spectrum Sensing; 1.1.3 Secure Compressive Spectrum Sensing; References; 2 Sparse Representation in Wireless Networks; 2.1 Principles of Standard Compressive Sensing; 2.1.1 Sparse Representation; 2.1.2 Projection; 2.1.3 Signal Reconstruction; 2.2 Reweighted Compressive Sensing; 2.3 Distributed Compressive Sensing; 2.4 Compressive Spectrum Sensing 2.4.1 Spectrum Sensing Methods2.4.2 Spectrum Sensing Model; 2.4.3 Compressive Wideband Spectrum Sensing; 2.4.3.1 Signals Arrives at Secondary Users; 2.4.3.2 Compressed Measurements Collection; 2.4.3.3 Signal Recovery; 2.4.3.4 Decision Making; 2.5 Summary; References; Part II Compressive Spectrum Sensing Algorithms; 3 Data-Driven Compressive Spectrum Sensing; 3.1 Introduction; 3.1.1 Related Work; 3.1.2 Contributions; 3.2 Data-Driven Compressive Spectrum Sensing Framework; 3.2.1 Iteratively Reweighted Least Square-Based Compressive Sensing 3.2.2 Non-iteratively Reweighted Least Square-Based Compressive Sensing3.2.2.1 Convergence Analyses; 3.2.2.2 Complexity Analyses; 3.2.3 Proposed Wilkinson's Method-Based DTT Location Probability Calculation Algorithm; 3.2.3.1 Maximum Allowable Equivalent Isotropic Radiated Power Calculation; 3.3 Numerical Analyses; 3.3.1 Numerical Analyses on Simulated Signals and Data; 3.3.2 Numerical Analyses on Real-World Signals and Data; 3.4 Summary; References; 4 Robust Compressive Spectrum Sensing; 4.1 Introduction; 4.1.1 Related Work; 4.1.2 Contributions 4.2 Robust Compressive Spectrum Sensing at Single User4.2.1 System Model; 4.2.1.1 Proposed Channel Division Scheme; 4.2.1.2 Proposed Denoised Spectrum Sensing Algorithm; 4.2.2 Computational Complexity and Spectrum Usage Analyses; 4.3 Numerical Analyses for Single User Case; 4.3.1 Analyses on Simulated Signals; 4.3.2 Analyses on Real-World Signals; 4.4 Matrix Completion-Based Robust Spectrum Sensing at Cooperative Multiple Users; 4.4.1 System Model; 4.4.1.1 Signals Arrive at Secondary Users; 4.4.1.2 Incomplete Matrix Construction at Fusion Center; 4.4.1.3 Matrix Completion at Fusion Center 4.4.1.4 Decision Making at an Fusion Center4.4.2 Denoised Cooperative Spectrum Sensing Algorithm; 4.4.3 Computational Complexity and Performance Analyses; 4.5 Numerical Analyses for Cooperative Multiple Users Case; 4.5.1 Analyses on Simulated Signals; 4.5.2 Analyses on Real-World Signals; 4.6 Summary; References; 5 Secure Compressive Spectrum Sensing; 5.1 Introduction; 5.1.1 Related Work; 5.1.2 Motivations and Contributions; 5.2 System Model; 5.2.1 Networks Description; 5.2.2 Signal Processing Model; 5.3 Malicious User Detection Framework; 5.3.1 Proposed Malicious User Detection Algorithm … (more)
- Publisher Details:
- London : Springer
- Publication Date:
- 2018
- Extent:
- 1 online resource
- Subjects:
- 006.2/5
Engineering
Wireless sensor networks
Internet of things
COMPUTERS / General
Technology & Engineering -- Telecommunications
Communications engineering / telecommunications
Wireless communication systems
Mobile communication systems
Telecommunication
Technology & Engineering -- Mobile & Wireless Communications
WAP (wireless) technology
Electronic books - Languages:
- English
- ISBNs:
- 9783030002909
- Related ISBNs:
- 303000290X
9783030002893 - Notes:
- Note: Online resource; title from PDF title page (EBSCO, viewed October 24, 2018)
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- Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
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- British Library HMNTS - ELD.DS.344238
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