Channel Estimation for Reconfigurable Intelligent Surface Aided Multi-User mmWave MIMO Systems

TL;DR

This paper introduces a novel, low-overhead channel estimation protocol for RIS-assisted multi-user mmWave MIMO systems, leveraging sparsity and joint recovery techniques to improve efficiency.

Contribution

It proposes a two-step joint channel estimation method exploiting common sparsity in cascaded channels, reducing training overhead in RIS-aided systems.

Findings

Effective estimation of cascaded channels with reduced training overhead

Utilization of common block sparsity for joint recovery

Improved accuracy in multi-user RIS-assisted MIMO systems

Abstract

Channel acquisition is one of the main challenges for the deployment of reconfigurable intelligent surface (RIS) aided communication systems. This is because an RIS has a large number of reflective elements, which are passive devices with no active transmitting/receiving abilities. In this paper, we study the channel estimation problem for the RIS aided multi-user millimeter-wave (mmWave) multi-input multi-output (MIMO) system. Specifically, we propose a novel channel estimation protocol for the above system to estimate the cascaded channels, which are the products of the channels from the base station (BS) to the RIS and from the RIS to the users. Further, since the cascaded channels are typically sparse, this allows us to formulate the channel estimation problem as a sparse recovery problem using compressive sensing (CS) techniques, thereby allowing the channels to be estimated with less training overhead. Moreover, the sparse channel matrices of the cascaded channels of all users have a common block sparsity structure due to the common channel between the BS and the RIS. To take advantage of the common sparsity pattern, we propose a two-step multi-user joint channel estimation procedure. In the first step, we make use of the common column-block sparsity and project the received signals onto the common column subspace. In the second step, we make use of the row-block sparsity of the projected signals and propose a multi-user joint sparse matrix recovery algorithm that takes into account the common channel between the BS and the RIS.