Matrix-Calibration-Based Cascaded Channel Estimation for Reconfigurable Intelligent Surface Assisted Multiuser MIMO

TL;DR

This paper introduces a matrix-calibration-based method for efficient channel estimation in RIS-assisted multiuser MIMO systems, reducing training overhead while maintaining high accuracy through a novel message-passing algorithm.

Contribution

It proposes a new matrix calibration approach for cascaded channel estimation that leverages channel sparsity and slow-varying components, with an analytical performance analysis.

Findings

High accuracy in channel estimation demonstrated in simulations

Reduced training overhead compared to state-of-the-art methods

Theoretical performance bounds established for the estimator

Abstract

Reconfigurable intelligent surface (RIS) is envisioned to be an essential component of the paradigm for beyond 5G networks as it can potentially provide similar or higher array gains with much lower hardware cost and energy consumption compared with the massive multiple-input multiple-output (MIMO) technology. In this paper, we focus on one of the fundamental challenges, namely the channel acquisition, in an RIS-assisted multiuser MIMO system. The state-of-the-art channel acquisition approach in such a system with fully passive RIS elements estimates the cascaded transmitter-to-RIS and RIS-to-receiver channels by adopting excessively long training sequences. To estimate the cascaded channels with an affordable training overhead, we formulate the channel estimation problem in the RIS-assisted multiuser MIMO system as a matrix-calibration based matrix factorization task. By exploiting the information on the slow-varying channel components and the hidden channel sparsity, we propose a novel message-passing based algorithm to factorize the cascaded channels. Furthermore, we present an analytical framework to characterize the theoretical performance bound of the proposed estimator in the large-system limit. Finally, we conduct simulations to verify the high accuracy and efficiency of the proposed algorithm.