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Machine learning (ML) can be used in various ways to improve multi-usermultiple-input multiple-output (MU-MIMO) receive processing. Typical approacheseither augment a single processing step, such as symbol detection, or replacemultiple steps jointly by a single neural network (NN). These techniquesdemonstrate promising results but often assume perfect channel stateinformation (CSI) or fail to satisfy the interpretability and scalabilityconstraints imposed by practical systems. In this paper, we propose a newstrategy which preserves the benefits of a conventional receiver, but enhancesspecific parts with ML components. The key idea is to exploit the orthogonalfrequency-division multiplexing (OFDM) signal structure to improve both thedemapping and the computation of the channel estimation error statistics.Evaluation results show that the proposed ML-enhanced receiver beats practicalbaselines on all considered scenarios, with significant gains at high speeds.

Machine Learning-enhanced Receive Processing for MU-MIMO OFDM Systems