Compressive sampling and adaptive multipath estimation

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buir.contributor.authorArıkan, Orhan
buir.contributor.orcidArıkan, Orhan|0000-0002-3698-8888
dc.citation.epage263en_US
dc.citation.spage260en_US
dc.contributor.authorPilancı, Merten_US
dc.contributor.authorArıkan, Orhanen_US
dc.coverage.spatialDiyarbakir, Turkeyen_US
dc.date.accessioned2016-02-08T12:21:35Z
dc.date.available2016-02-08T12:21:35Z
dc.date.issued2010en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.descriptionDate of Conference: 22-24 April 2010en_US
dc.description.abstractIn many signal processing problems such as channel estimation and equalization, the problem reduces to a linear system of equations. In this proceeding we formulate and investigate linear equations systems with sparse perturbations on the coefficient matrix. In a large class of matrices, it is possible to recover the unknowns exactly even if all the data, including the coefficient matrix and observation vector is corrupted. For this aim, we propose an optimization problem and derive its convex relaxation. The numerical results agree with the previous theoretical findings of the authors. The technique is applied to adaptive multipath estimation in cognitive radios and a significant performance improvement is obtained. The fact that rapidly varying channels are sparse in delay and doppler domain enables our technique to maintain reliable communication even far from the channel training intervals. ©2010 IEEE.en_US
dc.description.provenanceMade available in DSpace on 2016-02-08T12:21:35Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 70227 bytes, checksum: 26e812c6f5156f83f0e77b261a471b5a (MD5) Previous issue date: 2010en
dc.identifier.doi10.1109/SIU.2010.5650382en_US
dc.identifier.urihttp://hdl.handle.net/11693/28473
dc.language.isoTurkishen_US
dc.publisherIEEEen_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/SIU.2010.5650382en_US
dc.source.title2010 IEEE 18th Signal Processing and Communications Applications Conferenceen_US
dc.subjectCompressed sensingen_US
dc.subjectMatrix identificationen_US
dc.subjectSparse multipath channelsen_US
dc.subjectStructured perturbationsen_US
dc.subjectStructured total least squaresen_US
dc.subjectCompressed sensingen_US
dc.subjectMatrix identificationen_US
dc.subjectSparse multi-path channelen_US
dc.subjectStructured perturbationsen_US
dc.subjectStructured total least squaresen_US
dc.subjectEstimationen_US
dc.subjectLinear systemsen_US
dc.subjectMultipath propagationen_US
dc.subjectRelaxation processesen_US
dc.subjectSignal processingen_US
dc.subjectSignal reconstructionen_US
dc.subjectMatrix algebraen_US
dc.titleCompressive sampling and adaptive multipath estimationen_US
dc.title.alternativeSikiştirmali örnekleme ve uyarlamali çokyollu kestirimen_US
dc.typeConference Paperen_US

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