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dc.contributor.authorOzgor, Mehmet
dc.contributor.authorErküçük, Serhat
dc.contributor.authorCirpan, Hakan Ali
dc.date.accessioned2019-06-27T08:04:19Z
dc.date.available2019-06-27T08:04:19Z
dc.date.issued2012
dc.identifier.isbn9781467311182
dc.identifier.urihttps://hdl.handle.net/20.500.12469/925
dc.identifier.urihttps://doi.org/10.1109/TSP.2012.6256307
dc.description.abstractConsidering 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 l(1)-norm minimization based estimation which is widely used for sparse channel estimation. The study shows that BCS exhibits superior performance at higher SNR regions only for adequate number of measurements and sparser channel models (e. g. CM1 and CM2). Based on the results of this study BCS method or the l(1)-norm minimization method can be preferred over the other for different system implementation conditions.
dc.language.isoEnglish
dc.publisherIEEE
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectBayesian compressive sensing (BCS)
dc.subjectChannel models
dc.subjectl(1)-norm minimization
dc.subjectUltra-wideband (UWB) channel estimation
dc.titleBayesian compressive sensing for ultra-wideband channel models
dc.typeProceedings Paper
dc.identifier.startpage320
dc.identifier.endpage324
dc.relation.journal2012 35th International Conference on Telecommunications and Signal Processing (TSP)
dc.identifier.wosWOS:000308143100061
dc.identifier.doi10.1109/TSP.2012.6256307
dc.contributor.khasauthorErküçük, Serhat


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