Channel Estimation for RIS-Aided MU-MIMO mmWave Systems with Practical Hybrid Architecture

TL;DR

This paper introduces a low-overhead, three-stage channel estimation method for RIS-aided mmWave MU-MIMO systems with hybrid architectures, improving accuracy and reducing pilot overhead.

Contribution

It proposes a novel correlation-based three-stage estimation strategy that leverages angle invariance and channel decomposition to enhance efficiency in RIS-aided systems.

Findings

Achieves high estimation accuracy with fewer antennas.

Reduces pilot overhead by over five times compared to benchmarks.

Effective in hybrid RF architectures for mmWave MU-MIMO.

Abstract

This paper proposes a correlation-based three-stage channel estimation strategy with low pilot overhead for reconfigurable intelligent surface (RIS)-aided millimeter wave (mmWave) multi-user (MU) MIMO systems, in which both users and base station (BS) are equipped with a hybrid RF architecture. In Stage I, all users jointly transmit pilots and recover the uncompressed received signals to estimate the angle of arrival (AoA) at the BS using the discrete Fourier transform (DFT). Based on the observation that the overall cascaded MIMO channel can be decomposed into multiple sub-channels, the cascaded channel for a typical user is estimated in Stage II. Specifically, using the invariance of angles and the linear correlation of gains related to different cascaded subchannels, we use compressive sensing (CS), least squares (LS), and a one-dimensional search to estimate the Angles of Departure (AoDs), based on which the overall cascaded channel is obtained. In Stage III, the remaining users independently transmit pilots to estimate their individual cascaded channel with the same approach as in Stage II, which exploits the equivalent common RIS-BS channel obtained in Stage II to reduce the pilot overhead. In addition, the hybrid combining matrix and the RIS phase shift matrix are designed to reduce the noise power, thereby further improving the estimation performance. Simulation results demonstrate that the proposed algorithm can achieve high estimation accuracy especially when the number of antennas at the users is small, and reduce pilot overhead by more than five times compared with the existing benchmark approach.