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SpringerBriefs in Electrical and Computer Engineering

Data-Driven Wireless Networks

A Compressive Spectrum Approach

Authors: Gao, Yue, Qin, Zhijin

  • Presents the sparse representation in wireless communications with particular focus on data-driven compressive sensing in wideband cognitive radio networks
  • Provides a complete framework for compressive sensing in wideband cognitive radio networks, which is able to address the robustness, complexity, and security issues in spectrum sensing
  • The topic of compressive sensing and its applications in wireless networks are extremely hot worldwide
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eBook $54.99
price for USA in USD (gross)
  • ISBN 978-3-030-00290-9
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover $69.99
price for USA in USD
  • ISBN 978-3-030-00289-3
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
About this book

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.

Table of contents (6 chapters)

  • Introduction

    Gao, Yue (et al.)

    Pages 3-8

  • Sparse Representation in Wireless Networks

    Gao, Yue (et al.)

    Pages 9-20

  • Data-Driven Compressive Spectrum Sensing

    Gao, Yue (et al.)

    Pages 23-41

  • Robust Compressive Spectrum Sensing

    Gao, Yue (et al.)

    Pages 43-64

  • Secure Compressive Spectrum Sensing

    Gao, Yue (et al.)

    Pages 65-88

Buy this book

eBook $54.99
price for USA in USD (gross)
  • ISBN 978-3-030-00290-9
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover $69.99
price for USA in USD
  • ISBN 978-3-030-00289-3
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
Data-Driven Wireless Networks
Book Subtitle
A Compressive Spectrum Approach
Authors
Series Title
SpringerBriefs in Electrical and Computer Engineering
Copyright
2019
Publisher
Springer International Publishing
Copyright Holder
The Author(s), under exclusive license to Springer Nature Switzerland AG
eBook ISBN
978-3-030-00290-9
DOI
10.1007/978-3-030-00290-9
Softcover ISBN
978-3-030-00289-3
Series ISSN
2191-8112
Edition Number
1
Number of Pages
XIX, 93
Number of Illustrations
35 illustrations in colour
Topics