Improving Cooperative Spectrum Sensing under Correlated Log-normal Shadowing

Dinh, MT; Trang, CC; Nguyen, TQ; Nguyen, D

Improving Cooperative Spectrum Sensing under Correlated Log-normal Shadowing

Dinh, MT Trang, CC Nguyen, TQ Nguyen, D

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Publisher:: IEEE Computer Society
Publication Type:: Conference Proceeding
Citation:: Proceedings 2010 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery CyberC 2010, 2010, pp. 365 - 370
Issue Date:: 2010-01

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Field	Value	Language
dc.contributor.author	Dinh, MT	en_US
dc.contributor.author	Trang, CC	en_US
dc.contributor.author	Nguyen, TQ	en_US
dc.contributor.author	Nguyen, D	en_US
dc.contributor.editor	Technical Committee	en_US
dc.date	2010-10-10	en_US
dc.date.issued	2010-01	en_US
dc.identifier.citation	Proceedings 2010 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery CyberC 2010, 2010, pp. 365 - 370	en_US
dc.identifier.isbn	978-0-7695-4235-5	en_US
dc.identifier.uri	http://hdl.handle.net/10453/16167
dc.description.abstract	It is well known that exploiting spatial diversity via cooperation of many cognitive radios leads to an increase in energy detection reliability. This is because the probability that many radio channels concurrently experience the worst shadowing and fading channel conditions decreases as the number of cooperative CRs increases. Compared to multipath fading, shadowing effects tend to produce weak and correlated received signals, hence reducing spatial diversity gain over a much larger distance, depending on the characteristics and the size of the obstruction. The motivation of our paper is that because signals received by neighboring CRs subject to shadowing by the same obstacle or cluster of obstacles are highly correlated, these CRs should be excluded from the cooperative sensing group. In this paper we present a technique to successfully identify those CRs and how to quantify justification for their exclusion.	en_US
dc.publisher	IEEE Computer Society	en_US
dc.relation.ispartof	Proceedings 2010 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery CyberC 2010	en_US
dc.relation.ispartof	International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery	en_US
dc.relation.isbasedon	10.1109/CyberC.2010.72	en_US
dc.rights	© 2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.title	Improving Cooperative Spectrum Sensing under Correlated Log-normal Shadowing	en_US
dc.type	Conference Proceeding
utslib.location	USA	en_US
utslib.location.activity	Huangshan, China	en_US
utslib.for	0805 Distributed Computing	en_US
dc.location.activity	Huangshan, China	en_US
dc.location.activity	Melbourne, Australia
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.consider-herdc	true	en_US
pubs.place-of-publication	USA	en_US
pubs.start-date	2010-10-10	en_US

Abstract:

It is well known that exploiting spatial diversity via cooperation of many cognitive radios leads to an increase in energy detection reliability. This is because the probability that many radio channels concurrently experience the worst shadowing and fading channel conditions decreases as the number of cooperative CRs increases. Compared to multipath fading, shadowing effects tend to produce weak and correlated received signals, hence reducing spatial diversity gain over a much larger distance, depending on the characteristics and the size of the obstruction. The motivation of our paper is that because signals received by neighboring CRs subject to shadowing by the same obstacle or cluster of obstacles are highly correlated, these CRs should be excluded from the cooperative sensing group. In this paper we present a technique to successfully identify those CRs and how to quantify justification for their exclusion.

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http://hdl.handle.net/10453/16167