Low-Complexity Channel Estimation for RIS-Assisted Multi-User Wireless Communications

TL;DR

This paper introduces a low-overhead LMMSE channel estimation method for RIS-assisted multi-user wireless systems that leverages spatial correlation to improve accuracy while reducing pilot overhead.

Contribution

It proposes a novel linear MMSE estimator exploiting spatial correlation, reducing pilot overhead in RIS-assisted systems, and provides theoretical MSE analysis.

Findings

The proposed LMMSE estimator achieves lower MSE than existing grouping-based methods.

Theoretical normalized MSE is derived for the proposed estimator.

Numerical results confirm improved estimation accuracy with the new method.

Abstract

Reconfigurable intelligent surfaces (RISs) are eminently suitable for improving the reliability of wireless communications by jointly designing the active beamforming at the base station (BS) and the passive beamforming at the RIS. Therefore, the accuracy of channel estimation is crucial for RIS-aided systems. The challenge is that only the cascaded two-hop channel spanning from the user equipments (UEs) to the RIS and spanning from the RIS to the BS can be estimated, due to the lack of active radio frequency (RF) chains at RIS elements, which leads to high pilot overhead. In this paper, we propose a low-overhead linear minimum mean square error (LMMSE) channel estimation method by exploiting the spatial correlation of channel links, which strikes a trade-off between the pilot overhead and the channel estimation accuracy. Moreover, we calculate the theoretical normalized mean square error (MSE) for our channel estimation method. Finally, we verify numerically that the proposed LMMSE estimator has lower MSE than the state-of-the-art (SoA) grouping based estimators.