Cyclostationary Feature Detection Based Blind Approach for Spectrum Sensing and Classification

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dc.contributor.authorGeorge, Gemi Rachel
dc.contributor.authorPrema, Samuel Chris
dc.coverage.issue1cs
dc.coverage.volume28cs
dc.date.accessioned2020-04-23T06:56:47Z
dc.date.available2020-04-23T06:56:47Z
dc.date.issued2019-04cs
dc.description.abstractA Spectrum Sensing (SS) device, regardless of its location, should be able to detect the presence of signal over noise. In certain applications, SS should be able to correctly identify and classify the received signal. These functions are to be performed with little or no prior information about the incoming signal or channel noise. Cyclostationary Feature Detection (CFD) can be used to detect primary users (PU) using periodicity in autocorrelation of the modulated signals. These algorithms attempt to differentiate signal from noise based on the uncorrelated nature of noise. CFD is often considered as a semi-blind approach, since it requires prior information about the PU signal for detection. For identification and classification of PU signal, existing algorithms make use of CFD and neural networks. This paper proposes a novel algorithm to obtain completely blind detection performance based on CFD. Classification of PU signals is based on the basic statistics regarding cyclic spectrum. Further, an algorithm is formulated to identify modulation scheme of the signal and classify it without making use of any training algorithms. The proposed approach is capable of detecting PU reliably for SNR as low as –8 dB with no prior information about PU or noise in the channel.en
dc.formattextcs
dc.format.extent298-303cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationRadioengineering. 2019 vol. 28, č. 1, s. 298-303. ISSN 1210-2512cs
dc.identifier.doi10.13164/re.2019.0298en
dc.identifier.issn1210-2512
dc.identifier.urihttp://hdl.handle.net/11012/186858
dc.language.isoencs
dc.publisherSpolečnost pro radioelektronické inženýrstvícs
dc.relation.ispartofRadioengineeringcs
dc.relation.urihttps://www.radioeng.cz/fulltexts/2019/19_01_0298_0303.pdfcs
dc.rightsCreative Commons Attribution 4.0 International licenseen
dc.rights.accessopenAccessen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectSpectrum Sensing (SS)en
dc.subjectCyclostationary Feature Detection (CFD)en
dc.subjectSpectral Correlation Density function (SCD)en
dc.titleCyclostationary Feature Detection Based Blind Approach for Spectrum Sensing and Classificationen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.facultyFakulta eletrotechniky a komunikačních technologiícs
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