Sphericity minimum description length: asymptotic performance under unknown noise variance
Sphericity minimum description length: asymptotic performance under unknown noise variance
Citació
- 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
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Resum
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 literatureDescripció
Comunicació presentada a 2015 IEEE International Symposium on Information Theory (ISIT), celebrada del 14 al 19 de juny de 2015 a Hong Kong