Overview
- 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
Part of the book series: SpringerBriefs in Electrical and Computer Engineering (BRIEFSELECTRIC)
Access this book
Tax calculation will be finalised at checkout
Other ways to access
Table of contents (6 chapters)
-
Front Matter
-
Background
-
Front Matter
-
-
Compressive Spectrum Sensing Algorithms
-
Front Matter
-
-
Conclusions
-
Front Matter
-
Keywords
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.
Authors and Affiliations
Bibliographic Information
Book Title: Data-Driven Wireless Networks
Book Subtitle: A Compressive Spectrum Approach
Authors: Yue Gao, Zhijin Qin
Series Title: SpringerBriefs in Electrical and Computer Engineering
DOI: https://doi.org/10.1007/978-3-030-00290-9
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: The Author(s), under exclusive license to Springer Nature Switzerland AG 2019
Softcover ISBN: 978-3-030-00289-3Published: 07 November 2018
eBook ISBN: 978-3-030-00290-9Published: 19 October 2018
Series ISSN: 2191-8112
Series E-ISSN: 2191-8120
Edition Number: 1
Number of Pages: XIX, 93
Number of Illustrations: 35 illustrations in colour
Topics: Wireless and Mobile Communication, Communications Engineering, Networks