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

TL;DR

This paper introduces a machine learning framework for joint symbol detection and RIS configuration in multi-user MIMO systems, addressing channel estimation challenges without explicit channel knowledge.

Contribution

It proposes a Bayesian ML approach that jointly optimizes the RIS reflection patterns and receiver design using transmitted pilots, a novel method for RIS-aided systems.

Findings

Reliable communication in non-linear, noisy channels achieved

Joint optimization improves detection accuracy

No explicit channel estimation required

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.