Sphericity minimum description length: asymptotic performance under unknown noise variance

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dc.contributor.authorFont-Segura, Josep
dc.contributor.authorRiba, Jaume
dc.contributor.authorVázquez, Gregori
dc.date.accessioned2024-01-30T07:24:36Z
dc.date.available2024-01-30T07:24:36Z
dc.date.issued2015
dc.descriptionComunicació presentada a 2015 IEEE International Symposium on Information Theory (ISIT), celebrada del 14 al 19 de juny de 2015 a Hong Kong
dc.description.abstractThis paper revisits the model order selection problem in the context of second-order spectrum sensing in cognitive radio. Taking advantage of the recent interest on the generalized likelihood ratio (GLR), the asymptotic performance of the minimum description length (MDL) rule under unknown noise variance is addressed. In particular, by exploiting the asymptotically Chi-squared distribution of the GLR, a complete characterization of the error probability is reported, instead of approximating only the missed-detection probability as done in the literature
dc.description.sponsorshipThis work has been partially funded by the the Spanish Government (Ministerio de Econom´ıa y Competitividad) under TEC2010-21245-C02- 01 (DYNACS), TEC2013-47020C2-2-R COMPASS (COMPASS), CONSOLIDER INGENIO CSD2008-00010 (COMONSENS), CENIT CEN20101019 (THOFU), and the Catalan Government (AGAUR) under Grant 2014 SGR 60 and Fellowship FI-2010.
dc.format.mimetypeapplication/pdf
dc.identifier.citationFont-Segura J, Riba J, Vazquez G. Sphericity minimum description length: asymptotic performance under unknown noise variance. In: 2015 IEEE International Symposium on Information Theory (ISIT); 2015 Jun 14-19; Hong Kong. [Piscataway]: IEEE; 2015. p. 1615–19. DOI: 10.1109/ISIT.2015.7282729
dc.identifier.doihttp://dx.doi.org/10.1109/ISIT.2015.7282729
dc.identifier.isbn9781467377058
dc.identifier.issn2157-8095
dc.identifier.urihttp://hdl.handle.net/10230/58875
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof2015 IEEE International Symposium on Information Theory (ISIT); 2015 Jun 14-19; Hong Kong. [Piscataway]: IEEE; 2015. p. 1615–19.
dc.relation.projectIDinfo:eu-repo/grantAgreement/ES/3PN/CSD2008-00010
dc.relation.projectIDinfo:eu-repo/grantAgreement/ES/3PN/TEC2010-21245-C02-01
dc.relation.projectIDinfo:eu-repo/grantAgreement/ES/1PE/ TEC2013-47020C2-2-R
dc.rights© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. http://dx.doi.org/10.1109/ISIT.2015.7282729
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.subject.keywordModel order selection
dc.subject.keywordMinimum description length
dc.subject.keywordGeneralized likelihood ratio
dc.subject.keywordNoise uncertainty
dc.titleSphericity minimum description length: asymptotic performance under unknown noise variance
dc.typeinfo:eu-repo/semantics/conferenceObject
dc.type.versioninfo:eu-repo/semantics/acceptedVersion

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