Bregman divergence based sensor selections for spectrum sensing

Che, E; Tuan, HD; Kha, HH; Ngo, HQ

Bregman divergence based sensor selections for spectrum sensing

Che, E Tuan, HD

Kha, HH Ngo, HQ

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Publication Type:: Conference Proceeding
Citation:: IEEE Wireless Communications and Networking Conference, WCNC, 2012, pp. 2648 - 2652
Issue Date:: 2012-08-01

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Field	Value	Language
dc.contributor.author	Che, E	en_US
dc.contributor.author	Tuan, HD https://orcid.org/0000-0003-0292-6061	en_US
dc.contributor.author	Kha, HH	en_US
dc.contributor.author	Ngo, HQ	en_US
dc.date.issued	2012-08-01	en_US
dc.identifier.citation	IEEE Wireless Communications and Networking Conference, WCNC, 2012, pp. 2648 - 2652	en_US
dc.identifier.isbn	9781467304375	en_US
dc.identifier.issn	1525-3511	en_US
dc.identifier.uri	http://hdl.handle.net/10453/120918
dc.description.abstract	Sensor selection is to pick out an appropriate subset of active sensors for reliable collaborative sensing. Naturally, the selected sensors should be as uncorrelated as possible to have more independent sensing outputs for information fusion. In this paper, various uncorrelation metrics are unified by the concept of Bregman divergence. The sensor selections are then systematically formulated as NP-hard integer programs. Unlike commonly used exhaustive enumeration, heuristic searches or simple relaxation of discrete constraints with inherent drawbacks, this paper recasts them into a continuous d.c. (difference of two convex functions) program under convex constraints. Accordingly, an efficient iterative optimization procedure is tailored for locating the optimal solution. Simulation results show its superior performances in comparison with other existing sensor selections. © 2012 IEEE.	en_US
dc.relation.ispartof	IEEE Wireless Communications and Networking Conference, WCNC	en_US
dc.relation.isbasedon	10.1109/WCNC.2012.6214248	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Bregman divergence based sensor selections for spectrum sensing	en_US
dc.type	Conference Proceeding
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0805 Distributed Computing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
pubs.publication-status	Published	en_US

Abstract:

Sensor selection is to pick out an appropriate subset of active sensors for reliable collaborative sensing. Naturally, the selected sensors should be as uncorrelated as possible to have more independent sensing outputs for information fusion. In this paper, various uncorrelation metrics are unified by the concept of Bregman divergence. The sensor selections are then systematically formulated as NP-hard integer programs. Unlike commonly used exhaustive enumeration, heuristic searches or simple relaxation of discrete constraints with inherent drawbacks, this paper recasts them into a continuous d.c. (difference of two convex functions) program under convex constraints. Accordingly, an efficient iterative optimization procedure is tailored for locating the optimal solution. Simulation results show its superior performances in comparison with other existing sensor selections. © 2012 IEEE.

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http://hdl.handle.net/10453/120918