  Machine learning (ML) can be used in various ways to improve multi-user
multiple-input multiple-output (MU-MIMO) receive processing. Typical approaches
either augment a single processing step, such as symbol detection, or replace
multiple steps jointly by a single neural network (NN). These techniques
demonstrate promising results but often assume perfect channel state
information (CSI) or fail to satisfy the interpretability and scalability
constraints imposed by practical systems. In this paper, we propose a new
strategy which preserves the benefits of a conventional receiver, but enhances
specific parts with ML components. The key idea is to exploit the orthogonal
frequency-division multiplexing (OFDM) signal structure to improve both the
demapping and the computation of the channel estimation error statistics.
Evaluation results show that the proposed ML-enhanced receiver beats practical
baselines on all considered scenarios, with significant gains at high speeds.
