Bayesian sequential detection for the BSC with unknown crossover probability

Jill K. Nelson, Andrew Carl Singer

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We propose a novel scheme for detecting coded data transmitted over a communication channel that is either partially or entirely unknown. Viewing the unknown channel parameters as stochastic quantities drawn from a known probability distribution, the likelihood of a sequence of data is derived using Bayesian techniques. A stack-like tree search algorithm is proposed for implementation of maximum likelihood (ML) sequence detection under the Bayesian metric. We apply the Bayesian scheme to the binary symmetric channel (BSC) with unknown crossover probability. The structure of the resulting metric is compared to both the conventional Fano metric and a universal metric presented in (Lapidoth and Ziv, IEEE Trans. IT 1999). Based on its relationship to the metric developed by Lapidoth and Ziv, the newly-derived metric is shown to be pairwise universal over the ensemble of random uniform codes.

Original languageEnglish (US)
Title of host publicationProceedings - 2006 IEEE International Symposium on Information Theory, ISIT 2006
Pages640-644
Number of pages5
DOIs
StatePublished - Dec 1 2006
Event2006 IEEE International Symposium on Information Theory, ISIT 2006 - Seattle, WA, United States
Duration: Jul 9 2006Jul 14 2006

Publication series

NameIEEE International Symposium on Information Theory - Proceedings
ISSN (Print)2157-8101

Other

Other2006 IEEE International Symposium on Information Theory, ISIT 2006
CountryUnited States
CitySeattle, WA
Period7/9/067/14/06

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Information Systems
  • Modeling and Simulation
  • Applied Mathematics

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    Nelson, J. K., & Singer, A. C. (2006). Bayesian sequential detection for the BSC with unknown crossover probability. In Proceedings - 2006 IEEE International Symposium on Information Theory, ISIT 2006 (pp. 640-644). [4036041] (IEEE International Symposium on Information Theory - Proceedings). https://doi.org/10.1109/ISIT.2006.261863