Efficient Channel Estimation With Shorter Pilots in RIS-Aided Communications: Using Array Geometries and Interference Statistics

TL;DR

This paper introduces a novel RIS channel estimation method leveraging array geometries and interference statistics to reduce pilot length and improve accuracy in RIS-aided communications.

Contribution

It proposes a new reduced-subspace least squares (RS-LS) estimator that requires only array geometry knowledge, and optimizes RIS phase-shifts to minimize mean square error.

Findings

RS-LS estimator outperforms conventional LS estimator

Optimized RIS configurations significantly improve channel estimation accuracy

Reduced pilot length achieved by exploiting spatial correlation

Abstract

Accurate estimation of the cascaded channel from a user equipment (UE) to a base station (BS) via each reconfigurable intelligent surface (RIS) element is critical to realizing the full potential of the RIS's ability to control the overall channel. The number of parameters to be estimated is equal to the number of RIS elements, requiring an equal number of pilots unless an underlying structure can be identified. In this paper, we show how the spatial correlation inherent in the different RIS channels provides this desired structure. We first optimize the RIS phase-shift pattern using a much-reduced pilot length (determined by the rank of the spatial correlation matrices) to minimize the mean square error (MSE) in the channel estimation under electromagnetic interference. In addition to considering the linear minimum MSE (LMMSE) channel estimator, we propose a novel channel estimator that requires only knowledge of the array geometry while not requiring any user-specific statistical information. We call this the reduced-subspace least squares (RS-LS) estimator and optimize the RIS phase-shift pattern for it. This novel estimator significantly outperforms the conventional LS estimator. For both the LMMSE and RS-LS estimators, the proposed optimized RIS configurations result in significant channel estimation improvements over the benchmarks.