NDLI: Low-rank matrix completion based malicious user detection in cooperative spectrum sensing

Content Provider	IEEE Xplore Digital Library
Author	Zhijin Qin Yue Gao Plumbley, M.D. Parini, C.G. Cuthbert, L.G.
Copyright Year	2013
Description	Author affiliation: Electron. Eng. & Comput. Sci, Queen Mary Univ. of London, London, UK (Zhijin Qin; Yue Gao; Plumbley, M.D.; Parini, C.G.; Cuthbert, L.G.)
Abstract	In a cognitive radio (CR) system, cooperative spectrum sensing (CSS) is the key to improving sensing performance in deep fading channels. In CSS networks, signals received at the secondary users (SUs) are sent to a fusion center to make a final decision of the spectrum occupancy. In this process, the presence of malicious users sending false sensing samples can severely degrade the performance of the CSS network. In this paper, with the compressive sensing (CS) technique being implemented at each SU, we build a CSS network with double sparsity property. A new malicious user detection scheme is proposed by utilizing the adaptive outlier pursuit (AOP) based low-rank matrix completion in the CSS network. In the proposed scheme, the malicious users are removed in the process of signal recovery at the fusion center. The numerical analysis of the proposed scheme is carried out and compared with an existing malicious user detection algorithm.
Starting Page	1186
Ending Page	1189
File Size	332932
Page Count	4
File Format	PDF
ISBN	9781479902484
DOI	10.1109/GlobalSIP.2013.6737119
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fading Compressive sensing Cooperative spectrum sensing Collaboration Low-rank matrix completion Sensors Cognitive radio Sparse matrices Compressed sensing Malicious users Cascading style sheets
Content Type	Text
Resource Type	Article

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