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Reconfigurable Intelligent Surfaces (RISs) are regarded as a key technologyfor future wireless communications, enabling programmable radio propagationenvironments. However, the passive reflecting feature of RISs induces notablechallenges on channel estimation, making coherent symbol detection achallenging task. In this paper, we consider the uplink of RIS-aided multi-userMultiple-Input Multiple-Output (MIMO) systems and propose a Machine Learning(ML) approach to jointly design the multi-antenna receiver and configure theRIS reflection coefficients, which does not require explicit full knowledge ofthe channel input-output relationship. Our approach devises a ML-basedreceiver, while the configurations of the RIS reflection patterns affecting theunderlying propagation channel are treated as hyperparameters. Based on thissystem design formulation, we propose a Bayesian ML framework for optimizingthe RIS hyperparameters, according to which the transmitted pilots are directlyused to jointly tune the RIS and the multi-antenna receiver. Our simulationresults demonstrate the capability of the proposed approach to provide reliablecommunications in non-linear channel conditions corrupted by Gaussian noise.

Jointly Learned Symbol Detection and Signal Reflection in RIS-Aided Multi-user MIMO Systems