Font-Segura, JosepRiba, JaumeVázquez, Gregori2024-01-302024-01-302015Font-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.2015.728272997814673770582157-8095http://hdl.handle.net/10230/58875Comunicació presentada a 2015 IEEE International Symposium on Information Theory (ISIT), celebrada del 14 al 19 de juny de 2015 a Hong KongThis 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 literatureapplication/pdfeng© 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.2015.7282729info:eu-repo/semantics/conferenceObjecthttp://dx.doi.org/10.1109/ISIT.2015.7282729Model order selectionMinimum description lengthGeneralized likelihood ratioNoise uncertaintyinfo:eu-repo/semantics/openAccess