Bayesian ML sequence detection for ISI channels

Jill K. Nelson, Andrew C. Singer

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

Abstract

We propose a Bayesian technique for blind detection of coded data transmitted over a dispersive channel. The Bayesian maximum likelihood sequence detector views the channel taps as stochastic quantities drawn from a known distribution and computes the probability of any transmitted sequence by averaging over the tap values. The resulting path metric requires memory of all previous symbols, and hence a tree-based algorithm is employed to find the most likely transmitted sequence. Simulation results show that the Bayesian detector can achieve bit error rates within 1/4 dB of the conventional known-channel maximum likelihood (ML) sequence detector.

Original languageEnglish (US)
Title of host publication2006 IEEE Conference on Information Sciences and Systems, CISS 2006 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages693-698
Number of pages6
ISBN (Print)1424403502, 9781424403509
DOIs
StatePublished - Jan 1 2006
Event2006 40th Annual Conference on Information Sciences and Systems, CISS 2006 - Princeton, NJ, United States
Duration: Mar 22 2006Mar 24 2006

Publication series

Name2006 IEEE Conference on Information Sciences and Systems, CISS 2006 - Proceedings

Other

Other2006 40th Annual Conference on Information Sciences and Systems, CISS 2006
Country/TerritoryUnited States
CityPrinceton, NJ
Period3/22/063/24/06

ASJC Scopus subject areas

  • Computer Science(all)

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