Supervised Learning based Sparse Channel Estimation for RIS aided Communications

TL;DR

This paper introduces a supervised learning approach for sparse channel estimation in RIS-assisted millimeter wave communications, improving accuracy in both on-grid and off-grid AoA scenarios.

Contribution

It proposes a novel combination of sparse signal recovery and neural networks for enhanced uplink channel estimation in RIS-aided networks.

Findings

Performance gains demonstrated over existing methods

Effective estimation of direct and RIS channels

Supervised learning improves off-grid AoA estimation

Abstract

An reconfigurable intelligent surface (RIS) can be used to establish line-of-sight (LoS) communication when the direct path is compromised, which is a common occurrence in a millimeter wave (mmWave) network. In this paper, we focus on the uplink channel estimation of a such network. We formulate this as a sparse signal recovery problem, by discretizing the angle of arrivals (AoAs) at the base station (BS). On-grid and off-grid AoAs are considered separately. In the on-grid case, we propose an algorithm to estimate the direct and RIS channels. Neural networks trained based on supervised learning is used to estimate the residual angles in the off-grid case, and the AoAs in both cases. Numerical results show the performance gains of the proposed algorithms in both cases.