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Diversified Compressed Spectrum Sensing for Recovery Noise Reduction

Chae, Daniel H; Sadeghi, Parastoo; Kennedy, Rodney; Yang, Janghoon

Description

We propose a method to reduce the spectrum noise when compressed sensing (CS) is applied to spectrum sensing. Since CS is susceptible to noise, the quality of the recovered spectrum using CS can be significantly degraded if the measurements are contaminated with noise. This will be particularly problematic in case of detecting weak signals. In this paper, a method which exploits the diversity of CS measurements is introduced to reduce the noise of CS recovered spectrum. Diversity gain is...[Show more]

dc.contributor.authorChae, Daniel H
dc.contributor.authorSadeghi, Parastoo
dc.contributor.authorKennedy, Rodney
dc.contributor.authorYang, Janghoon
dc.coverage.spatialSydney Australia
dc.date.accessioned2015-12-10T23:17:39Z
dc.date.createdSeptember 9-12 2012
dc.identifier.isbn9781467325691
dc.identifier.urihttp://hdl.handle.net/1885/65307
dc.description.abstractWe propose a method to reduce the spectrum noise when compressed sensing (CS) is applied to spectrum sensing. Since CS is susceptible to noise, the quality of the recovered spectrum using CS can be significantly degraded if the measurements are contaminated with noise. This will be particularly problematic in case of detecting weak signals. In this paper, a method which exploits the diversity of CS measurements is introduced to reduce the noise of CS recovered spectrum. Diversity gain is extracted from measurements using only a single physical sensor, but with virtual parallel branches. The results show that the noise is reduced sufficiently to detect weak signals using our method.
dc.publisherIEEE Communications Society
dc.relation.ispartofseriesIEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC 2012)
dc.subjectKeywords: Compressed spectrum sensing; Compressive sensing; Diversity gain; Physical sensors; Spectrum sensing; Weak signals; Recovery; Signal detection
dc.titleDiversified Compressed Spectrum Sensing for Recovery Noise Reduction
dc.typeConference paper
local.description.notesImported from ARIES
local.description.refereedYes
dc.date.issued2012
local.identifier.absfor100510 - Wireless Communications
local.identifier.absfor080401 - Coding and Information Theory
local.identifier.ariespublicationu4334215xPUB1086
local.type.statusPublished Version
local.contributor.affiliationChae, Daniel H, College of Engineering and Computer Science, ANU
local.contributor.affiliationSadeghi, Parastoo, College of Engineering and Computer Science, ANU
local.contributor.affiliationKennedy, Rodney, College of Engineering and Computer Science, ANU
local.contributor.affiliationYang, Janghoon, Korean-German Institute of Technology
local.description.embargo2037-12-31
local.bibliographicCitation.startpage2149
local.bibliographicCitation.lastpage2154
local.identifier.doi10.1109/PIMRC.2012.6362710
local.identifier.absseo970109 - Expanding Knowledge in Engineering
dc.date.updated2016-02-24T10:57:23Z
local.identifier.scopusID2-s2.0-84871970387
CollectionsANU Research Publications

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