Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/48780
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dc.contributor.authorChandrasekhar, K-
dc.contributor.authorKumaraswamy, H V-
dc.date.accessioned2019-07-05T05:59:21Z-
dc.date.available2019-07-05T05:59:21Z-
dc.date.issued2019-07-
dc.identifier.issn0975-1084 (Online); 0022-4456 (Print)-
dc.identifier.urihttp://nopr.niscair.res.in/handle/123456789/48780-
dc.description473-477en_US
dc.description.abstractSpectrum sensing is one of the enabling functionalities for cognitive radio systems to operate in the spectrum white space. To protect the primary incumbent users from interference the Cognitive Radio (CR) is required to detect the incumbent signals at a very low SNR. In this paper spectrum sensing algorithms are proposed based on the sampled correlation matrix calculated from the received signal samples. The computed correlation matrix is used to estimate the frequency function of the Pisarenko Harmonic Decomposition (PHD) method and Music-like algorithms to detect the occupied and unoccupied channels. The salient feature of this approach is that, no prior knowledge of signal properties (which would lead to uncertainly problems) are necessary. Further, the efficiency of this method is assessed by calculating the detection probability of the occupied channel as a function of the signal to noise ratio of random input signals. The simulation results demonstrate a reliable detection, at a low SNR.en_US
dc.language.isoen_USen_US
dc.publisherNISCAIR-CSIR, Indiaen_US
dc.rights CC Attribution-Noncommercial-No Derivative Works 2.5 Indiaen_US
dc.sourceJSIR Vol.78(07) [July 2019]en_US
dc.subjectCognitive radioen_US
dc.subjectMulticoset samplingen_US
dc.subjectNoise subspaceen_US
dc.subjectSpectrum sensingen_US
dc.titlePerformance Evaluation of Pisarenko Harmonic Decomposition and Music-Like Algorithms for Narrowband Spectrum Sensing in Cognitive Radioen_US
dc.typeArticleen_US
Appears in Collections:JSIR Vol.78(07) [July 2019]

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