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In orthogonal frequency division multiplexing (OFDM)-based wirelesscommunication systems, the bit error rate (BER) performance is heavilydependent on the accuracy of channel estimation. It is important for a goodchannel estimator to be capable of handling the changes in the wireless channelconditions that occur due to the mobility of the users. In recent years, thefocus has been on developing complex neural network (NN)- based channelestimators that enable an error performance close to that of a genie-aidedchannel estimator. This work considers the other alternative which is to have asimple channel estimator but a more complex NN-based demapper for thegeneration of soft information for each transmitted bit. In particular, theproblem of reversing the adverse effects of an imperfect channel estimator isaddressed, and a convolutional self-attention-based neural demapper thatsignificantly outperforms the baseline is proposed.

Convolutional Self-Attention-Based Multi-User MIMO Demapper