Convolutional Self-Attention-Based Multi-User MIMO Demapper

TL;DR

This paper proposes a convolutional self-attention neural demapper for OFDM systems that compensates for imperfect channel estimation, significantly improving bit error rate performance over traditional methods.

Contribution

It introduces a novel self-attention-based neural demapper that enhances decoding accuracy without relying on complex channel estimators.

Findings

Outperforms baseline demappers in BER performance

Effectively mitigates errors from imperfect channel estimation

Demonstrates robustness in varying channel conditions

Abstract

In orthogonal frequency division multiplexing (OFDM)-based wireless communication systems, the bit error rate (BER) performance is heavily dependent on the accuracy of channel estimation. It is important for a good channel estimator to be capable of handling the changes in the wireless channel conditions that occur due to the mobility of the users. In recent years, the focus has been on developing complex neural network (NN)- based channel estimators that enable an error performance close to that of a genie-aided channel estimator. This work considers the other alternative which is to have a simple channel estimator but a more complex NN-based demapper for the generation of soft information for each transmitted bit. In particular, the problem of reversing the adverse effects of an imperfect channel estimator is addressed, and a convolutional self-attention-based neural demapper that significantly outperforms the baseline is proposed.