Channel Decoding with a Bayesian Equalizer

TL;DR

This paper introduces a Bayesian equalizer that accounts for both channel noise and CSI estimation uncertainty to enhance LDPC decoding performance, reducing bit error rates in practical scenarios.

Contribution

It presents a novel Bayesian equalizer that improves APP estimates by considering CSI uncertainty, enhancing LDPC decoding accuracy.

Findings

Bayesian equalizer reduces BER compared to traditional methods.

Accounting for CSI uncertainty improves decoding robustness.

Experimental results demonstrate significant performance gains.

Abstract

Low-density parity-check (LPDC) decoders assume the channel estate information (CSI) is known and they have the true a posteriori probability (APP) for each transmitted bit. But in most cases of interest, the CSI needs to be estimated with the help of a short training sequence and the LDPC decoder has to decode the received word using faulty APP estimates. In this paper, we study the uncertainty in the CSI estimate and how it affects the bit error rate (BER) output by the LDPC decoder. To improve these APP estimates, we propose a Bayesian equalizer that takes into consideration not only the uncertainty due to the noise in the channel, but also the uncertainty in the CSI estimate, reducing the BER after the LDPC decoder.