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dc.contributor.authorAhmed, A.*
dc.contributor.authorHu, Yim Fun*
dc.contributor.authorNoras, James M.*
dc.date.accessioned2016-11-23T18:21:06Z
dc.date.available2016-11-23T18:21:06Z
dc.date.issued2014
dc.identifier.citationAhmed A, Hu YF and Noras JM (2014) Enhanced energy detection based spectrum sensing in cognitive radio networks using Random Matrix Theory. In: 9th International Symposium on Communication Systems, Networks and Digital Signal Processing. 23-25 Jul 2014, Manchester, UK: 384-389.
dc.identifier.urihttp://hdl.handle.net/10454/10575
dc.descriptionNo
dc.description.abstractSpectrum sensing is used in cognitive radio systems to detect the availability of spectrum holes for secondary usage. The simplest and most famous spectrum sensing techniques are based either on energy detection or eigenspace analysis from Random Matrix Theory (RMT) such as using the Marchenko-Pastur law. These schemes suffer from uncertainty in estimating the noise variance which reduces their performance. In this paper we propose a new method to evaluate the noise variance that can eliminate the limitations of the aforementioned schemes. This method estimates the noise variance from a measurement set of noisy signals or noise-only signals. Extensive simulations show that the proposed method performs well in estimating the noise variance. Its performance greatly improves with increasing numbers of measurements and also with increasing numbers of samples taken per measurement.
dc.relation.isreferencedbyhttps://doi.org/10.1016/j.aasri.2014.09.008
dc.subjectNoise estimation and analysis; Spectrum sensing; Cognitive radio networks; Random matrix theory; Algorithms
dc.titleNoise Variance Estimation for Spectrum Sensing in Cognitive Radio Networks
dc.status.refereedYes
dc.typeArticle
dc.type.versionNo full-text in the repository


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