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.
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.