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

Liuhang Wang; Nir Shlezinger; George C. Alexandropoulos; Haiyang Zhang; Baoyun Wang; Yonina C. Elda

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arXiv · 2202.06663

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

Liuhang Wang·Nir Shlezinger·George C. Alexandropoulos·Haiyang Zhang·Baoyun Wang·Yonina C. Elda

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Abstract

Reconfigurable Intelligent Surfaces (RISs) are regarded as a key technology for future wireless communications, enabling programmable radio propagation environments. However, the passive reflecting feature of RISs induces notable challenges on channel estimation, making coherent symbol detection a challenging task. In this paper, we consider the uplink of RIS-aided multi-user Multiple-Input Multiple-Output (MIMO) systems and propose a Machine Learning (ML) approach to jointly design the multi-antenna receiver and configure the RIS reflection coefficients, which does not require explicit full knowledge of the channel input-output relationship. Our approach devises a ML-based receiver, while the configurations of the RIS reflection patterns affecting the underlying propagation channel are treated as hyperparameters. Based on this system design formulation, we propose a Bayesian ML framework for optimizing the RIS hyperparameters, according to which the transmitted pilots are directly used to jointly tune the RIS and the multi-antenna receiver. Our simulation results demonstrate the capability of the proposed approach to provide reliable communications in non-linear channel conditions corrupted by Gaussian noise.

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Liuhang Wang, Nir Shlezinger, George C. Alexandropoulos, Haiyang Zhang, Baoyun Wang, Yonina C. Elda. 2022-02-14. Jointly Learned Symbol Detection and Signal Reflection in RIS-Aided Multi-user MIMO Systems. https://arxiv.org/abs/2202.06663

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