Compressed CPD-Based Channel Estimation and Joint Beamforming for RIS-Assisted Millimeter Wave Communications

TL;DR

This paper introduces CPD-based channel estimation and joint beamforming techniques for RIS-assisted mmWave MIMO-OFDM systems, significantly reducing training overhead by exploiting low-rank tensor structures and channel sparsity.

Contribution

It proposes novel CPD-based methods for cascade channel estimation that leverage low-rank tensor decomposition, improving accuracy and reducing training complexity in RIS-assisted mmWave systems.

Findings

Achieves near-CRB mean square error in channel estimation.

Reduces training overhead through low-rank tensor modeling.

Enhances beamforming performance using estimated channels.

Abstract

We consider the problem of channel estimation and joint active and passive beamforming for reconfigurable intelligent surface (RIS) assisted millimeter wave (mmWave) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. We show that, with a well-designed frame-based training protocol, the received pilot signal can be organized into a low-rank third-order tensor that admits a canonical polyadic decomposition (CPD). Based on this observation, we propose two CPD-based methods for estimating the cascade channels associated with different subcarriers. The proposed methods exploit the intrinsic low-rankness of the CPD formulation, which is a result of the sparse scattering characteristics of mmWave channels, and thus have the potential to achieve a significant training overhead reduction. Specifically, our analysis shows that the proposed methods have a sample complexity that scales quadratically with the sparsity of the cascade channel. Also, by utilizing the singular value decomposition-like structure of the effective channel, this paper develops a joint active and passive beamforming method based on the estimated cascade channels. Simulation results show that the proposed CPD-based channel estimation methods attain mean square errors that are close to the Cramer-Rao bound (CRB) and present a clear advantage over the compressed sensing-based method. In addition, the proposed joint beamforming method can effectively utilize the estimated channel parameters to achieve superior beamforming performance.