Bayesian Compressive Sensing for Primary User Detection

dc.contributor.authorBaşaran, Mehmet
dc.contributor.authorErküçük, Serhat
dc.contributor.authorCirpan, Hakan Ali
dc.date.accessioned2019-06-27T08:01:45Z
dc.date.available2019-06-27T08:01:45Z
dc.date.issued2016
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.description.abstractIn 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.citation13
dc.identifier.doi10.1049/iet-spr.2015.0529en_US
dc.identifier.endpage523
dc.identifier.issn1751-9675en_US
dc.identifier.issn1751-9683en_US
dc.identifier.issn1751-9675
dc.identifier.issn1751-9683
dc.identifier.issue5
dc.identifier.scopus2-s2.0-84974577904en_US
dc.identifier.scopusqualityQ3
dc.identifier.startpage514en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12469/460
dc.identifier.urihttps://doi.org/10.1049/iet-spr.2015.0529
dc.identifier.volume10en_US
dc.identifier.wosWOS:000378724800011en_US
dc.institutionauthorErküçük, Serhaten_US
dc.institutionauthorErküçük, Serhat
dc.language.isoenen_US
dc.publisherInst Engineering Technology-IETen_US
dc.relation.journalIET Signal Processingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCompressed Sensingen_US
dc.subjectRadio Spectrum Managementen_US
dc.subjectSignal Detectionen_US
dc.subjectBayes Methodsen_US
dc.subjectSignal Reconstructionen_US
dc.subjectIterative Methodsen_US
dc.subjectBayesian Compressive Sensingen_US
dc.subjectPrimary User Detection Probabilityen_US
dc.subjectPrimary User Signal Reconstructionen_US
dc.subjectBayesian CS-Based Spectrum Sensingen_US
dc.subjectFalse Alarm Probabilityen_US
dc.subjectIteration Methoden_US
dc.subjectSignal-to-noise Ratioen_US
dc.subjectCompression Ratioen_US
dc.titleBayesian Compressive Sensing for Primary User Detectionen_US
dc.typeArticleen_US
dspace.entity.typePublication
relation.isAuthorOfPublication440e977b-46c6-40d4-b970-99b1e357c998
relation.isAuthorOfPublication.latestForDiscovery440e977b-46c6-40d4-b970-99b1e357c998

Files