  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.
