Maximum Likelihood Channel Estimation for RIS-Aided Communications With LOS Channels

TL;DR

This paper introduces a maximum likelihood channel estimation method for RIS-assisted communications with LOS channels, enabling accurate channel estimation with minimal pilots by adaptively selecting RIS configurations.

Contribution

It develops a novel parametric ML estimation framework that efficiently estimates LOS channels in RIS systems with limited pilot resources.

Findings

Accurately estimates LOS channels with few pilots

Uses adaptive RIS configurations to enhance estimation accuracy

Demonstrates effectiveness through simulations or experiments

Abstract

A reconfigurable intelligent surface (RIS) reflects incoming signals in different ways depending on the phase-shift pattern assigned to its elements. The most promising use case is to aid the communication between a base station and a user when the user has a line-of-sight (LOS) channel to the RIS but the direct channel is blocked. The main challenge is to estimate the channel with limited resources because non-parametric estimation methods require a pilot length proportional to the large number of RIS elements. In this paper, we develop a parametric maximum likelihood (ML) channel estimation framework for estimating the LOS channel to the RIS. We demonstrate that the proposed algorithm can accurately obtain the channel and preferred RIS configuration using only a few pilots. A key novelty is that the RIS configurations used during pilot transmission are selected to progressively improve the estimation accuracy.