  Machine learning (ML) starts to be widely used to enhance the performance of
multi-user multiple-input multiple-output (MU-MIMO) receivers. However, it is
still unclear if such methods are truly competitive with respect to
conventional methods in realistic scenarios and under practical constraints. In
addition to enabling accurate signal reconstruction on realistic channel
models, MU-MIMO receive algorithms must allow for easy adaptation to a varying
number of users without the need for retraining. In contrast to existing work,
we propose an ML-enhanced MU-MIMO receiver that builds on top of a conventional
linear minimum mean squared error (LMMSE) architecture. It preserves the
interpretability and scalability of the LMMSE receiver, while improving its
accuracy in two ways. First, convolutional neural networks (CNNs) are used to
compute an approximation of the second-order statistics of the channel
estimation error which are required for accurate equalization. Second, a
CNN-based demapper jointly processes a large number of orthogonal
frequency-division multiplexing (OFDM) symbols and subcarriers, which allows it
to compute better log likelihood ratios (LLRs) by compensating for channel
aging. The resulting architecture can be used in the up- and downlink and is
trained in an end-to-end manner, removing the need for hard-to-get perfect
channel state information (CSI) during the training phase. Simulation results
demonstrate consistent performance improvements over the baseline which are
especially pronounced in high mobility scenarios.
