Sphericity minimum description length: asymptotic performance under unknown noise variance

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• dc.contributor.author Font-Segura, Josep
• dc.contributor.author Riba, Jaume
• dc.contributor.author Vázquez, Gregori
• dc.date.accessioned 2024-01-30T07:24:36Z
• dc.date.available 2024-01-30T07:24:36Z
• dc.date.issued 2015
• dc.description Comunicació presentada a 2015 IEEE International Symposium on Information Theory (ISIT), celebrada del 14 al 19 de juny de 2015 a Hong Kong
• dc.description.abstract This 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.sponsorship This 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.mimetype application/pdf
• dc.identifier.citation Font-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.doi http://dx.doi.org/10.1109/ISIT.2015.7282729
• dc.identifier.isbn 9781467377058
• dc.identifier.issn 2157-8095
• dc.identifier.uri http://hdl.handle.net/10230/58875
• dc.language.iso eng
• dc.publisher Institute of Electrical and Electronics Engineers (IEEE)
• dc.relation.ispartof 2015 IEEE International Symposium on Information Theory (ISIT); 2015 Jun 14-19; Hong Kong. [Piscataway]: IEEE; 2015. p. 1615–19.
• dc.relation.projectID info:eu-repo/grantAgreement/ES/3PN/CSD2008-00010
• dc.relation.projectID info:eu-repo/grantAgreement/ES/3PN/TEC2010-21245-C02-01
• dc.relation.projectID info: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.accessRights info:eu-repo/semantics/openAccess
• dc.subject.keyword Model order selection
• dc.subject.keyword Minimum description length
• dc.subject.keyword Generalized likelihood ratio
• dc.subject.keyword Noise uncertainty
• dc.title Sphericity minimum description length: asymptotic performance under unknown noise variance
• dc.type info:eu-repo/semantics/conferenceObject
• dc.type.version info:eu-repo/semantics/acceptedVersion