Data-driven wireless networks : a compressive spectrum approach /

Data-driven wireless networks : a compressive spectrum approach /

Saved in:

Bibliographic Details
Author / Creator:	Gao, Yue, author.
Imprint:	London : Springer, [2019]
Description:	1 online resource
Language:	English
Series:	SpringerBriefs in electrical and computer engineering SpringerBriefs in electrical and computer engineering.
Subject:	Wireless sensor networks. Internet of things. Internet of things. Wireless sensor networks.
Format:	E-Resource Book
URL for this record:	http://pi.lib.uchicago.edu/1001/cat/bib/11746050

Hidden Bibliographic Details
Other authors / contributors:	Qin, Zhijin, author.
ISBN:	9783030002909 303000290X 9783030002893 3030002896
Notes:	Print version record.
Summary:	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.
Other form:	Print version: Gao, Yue. Data-driven wireless networks. London : Springer, [2019] 3030002896 9783030002893

Table of Contents:

Part I : Background
Introduction
Sparse representation in wireless networks
Part II : Compressive spectrum sensing algorithms
Data-driven compressive spectrum sensing
Robust compressive spectrum sensing
Secure compressive spectrum sensing
Part III : Conclusions
Conclusions and future work.