Bayesian Compressive Sensing for Ultra-Wideband Channel Estimation: Algorithm and Performance Analysis

dc.contributor.author Özgör, Mehmet
dc.contributor.author Erküçük, Serhat
dc.contributor.author Erküçük, Serhat
dc.contributor.author Çırpan, Hakan Ali
dc.contributor.other Electrical-Electronics Engineering
dc.date.accessioned 2019-06-27T08:02:14Z
dc.date.available 2019-06-27T08:02:14Z
dc.date.issued 2015
dc.department Fakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümü en_US
dc.description.abstract Due to the sparse structure of ultra-wideband (UWB) channels compressive sensing (CS) is suitable for UWB channel estimation. Among various implementations of CS the inclusion of Bayesian framework has shown potential to improve signal recovery as statistical information related to signal parameters is considered. In this paper we study the channel estimation performance of Bayesian CS (BCS) for various UWB channel models and noise conditions. Specifically we investigate the effects of (i) sparse structure of standardized IEEE 802.15.4a channel models (ii) signal-to-noise ratio (SNR) regions and (iii) number of measurements on the BCS channel estimation performance and compare them to the results of -norm minimization based estimation which is widely used for sparse channel estimation. We also provide a lower bound on mean-square error (MSE) for the biased BCS estimator and compare it with the MSE performance of implemented BCS estimator. Moreover we study the computation efficiencies of BCS and -norm minimization in terms of computation time by making use of the big- notation. The study shows that BCS exhibits superior performance at higher SNR regions for adequate number of measurements and sparser channel models (e.g. CM-1 and CM-2). Based on the results of this study the BCS method or the -norm minimization method can be preferred over the other one for different system implementation conditions. en_US]
dc.identifier.citationcount 3
dc.identifier.doi 10.1007/s11235-014-9902-7 en_US
dc.identifier.endpage 427
dc.identifier.issn 1018-4864 en_US
dc.identifier.issn 1572-9451 en_US
dc.identifier.issn 1018-4864
dc.identifier.issn 1572-9451
dc.identifier.issue 4
dc.identifier.scopus 2-s2.0-84933278608 en_US
dc.identifier.scopusquality Q2
dc.identifier.startpage 417 en_US
dc.identifier.uri https://hdl.handle.net/20.500.12469/579
dc.identifier.uri https://doi.org/10.1007/s11235-014-9902-7
dc.identifier.volume 59 en_US
dc.identifier.wos WOS:000356933400002 en_US
dc.institutionauthor Erküçük, Serhat en_US
dc.language.iso en en_US
dc.publisher Springer en_US
dc.relation.journal Telecommunication Systems en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.scopus.citedbyCount 3
dc.subject Bayesian compressive sensing (BCS) en_US
dc.subject IEEE 802.15.4a channel models en_US
dc.subject l(1)-norm minimization en_US
dc.subject Mean-square error (MSE) lower bound en_US
dc.subject Ultra-wideband (UWB) channel estimation en_US
dc.title Bayesian Compressive Sensing for Ultra-Wideband Channel Estimation: Algorithm and Performance Analysis en_US
dc.type Article en_US
dc.wos.citedbyCount 3
dspace.entity.type Publication
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relation.isAuthorOfPublication.latestForDiscovery 440e977b-46c6-40d4-b970-99b1e357c998
relation.isOrgUnitOfPublication 12b0068e-33e6-48db-b92a-a213070c3a8d
relation.isOrgUnitOfPublication.latestForDiscovery 12b0068e-33e6-48db-b92a-a213070c3a8d

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