Bayesian Compressive Sensing for Primary User Detection

dc.contributor.author Başaran, Mehmet
dc.contributor.author Erküçük, Serhat
dc.contributor.author Erküçük, Serhat
dc.contributor.author Cirpan, Hakan Ali
dc.contributor.other Electrical-Electronics Engineering
dc.date.accessioned 2019-06-27T08:01:45Z
dc.date.available 2019-06-27T08:01:45Z
dc.date.issued 2016
dc.department Fakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümü en_US
dc.description.abstract In compressive sensing (CS)-based spectrum sensing literature most studies consider accurate reconstruction of the primary user signal rather than detection of the signal. Furthermore possible absence of the signal is not taken into account while evaluating the spectrum sensing performance. In this study Bayesian CS is studied in detail for primary user detection. In addition to assessing the signal reconstruction performance and comparing it with the conventional basis pursuit approach and the corresponding lower bounds signal detection performance is also considered both analytically and through simulation studies. In the absence of a primary user signal the trade-off between probabilities of detection and false alarm is studied as it is equally important to determine the performance of a CS approach when there is no active primary user. To reduce the computation time and yet achieve a similar detection performance finally the effect of number of iterations is studied for various systems parameters including signal-to-noise-ratio compression ratio mean value of accumulated energy and threshold values. The presented framework in this study is important in the overall implementation of CS-based approaches for primary user detection in practical realisations such as LTE downlink OFDMA as it considers both signal reconstruction and detection. en_US]
dc.identifier.citationcount 13
dc.identifier.doi 10.1049/iet-spr.2015.0529 en_US
dc.identifier.endpage 523
dc.identifier.issn 1751-9675 en_US
dc.identifier.issn 1751-9683 en_US
dc.identifier.issn 1751-9675
dc.identifier.issn 1751-9683
dc.identifier.issue 5
dc.identifier.scopus 2-s2.0-84974577904 en_US
dc.identifier.scopusquality Q3
dc.identifier.startpage 514 en_US
dc.identifier.uri https://hdl.handle.net/20.500.12469/460
dc.identifier.uri https://doi.org/10.1049/iet-spr.2015.0529
dc.identifier.volume 10 en_US
dc.identifier.wos WOS:000378724800011 en_US
dc.institutionauthor Erküçük, Serhat en_US
dc.language.iso en en_US
dc.publisher Inst Engineering Technology-IET en_US
dc.relation.journal IET Signal Processing 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 16
dc.subject Compressed Sensing en_US
dc.subject Radio Spectrum Management en_US
dc.subject Signal Detection en_US
dc.subject Bayes Methods en_US
dc.subject Signal Reconstruction en_US
dc.subject Iterative Methods en_US
dc.subject Bayesian Compressive Sensing en_US
dc.subject Primary User Detection Probability en_US
dc.subject Primary User Signal Reconstruction en_US
dc.subject Bayesian CS-Based Spectrum Sensing en_US
dc.subject False Alarm Probability en_US
dc.subject Iteration Method en_US
dc.subject Signal-to-noise Ratio en_US
dc.subject Compression Ratio en_US
dc.title Bayesian Compressive Sensing for Primary User Detection en_US
dc.type Article en_US
dc.wos.citedbyCount 14
dspace.entity.type Publication
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relation.isOrgUnitOfPublication.latestForDiscovery 12b0068e-33e6-48db-b92a-a213070c3a8d

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