PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Bayesian BCJR for Channel Equalization and Decoding
Luis Salamanca, Juan Jose Murillo-Fuentes and Fernando Perez-Cruz
In: 2010 IEEE International Workshop on MACHINE LEARNING FOR SIGNAL PROCESSING, August 29 - September 1, 2010, Kittilä, Finland.

Abstract

In this paper we focus on the probabilistic channel equalization in digital communications. We face the single input single output (SISO) model to show how the statistical information about the multipath channel can be exploited to further improve our estimation of the a posteriori probabilities (APP) during the equalization process. We consider not only the uncertainty due to the noise in the channel, but also in the estimate of the channel estate information (CSI). Thus, we resort to a Bayesian approach for the computation of the APP. This novel algorithm has the same complexity as the BCJR, exhibiting lower bit error rate at the output of the channel decoder than the standard BCJR that considers maximum likelihood (ML) to estimate the CSI.

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EPrint Type:Conference or Workshop Item (Talk)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:7551
Deposited By:Fernando Perez-Cruz
Deposited On:17 March 2011