Channel Estimation for Hybrid RIS Aided MIMO Communications via Atomic Norm Minimization

TL;DR

This paper proposes a novel channel estimation method for hybrid RIS-assisted MIMO systems using atomic norm minimization, improving accuracy over passive RIS schemes with the same training effort.

Contribution

It introduces a two-stage off-the-grid compressive sensing approach for channel estimation in hybrid RIS MIMO systems, enhancing performance and overcoming passive RIS limitations.

Findings

Outperforms passive RIS channel estimation with same training overhead

Uses atomic norm minimization for precise channel parameter extraction

Demonstrates effectiveness through simulation results

Abstract

Reconfigurable intelligent surfaces (RISs) have been introduced as a remedy for mitigating frequent blockages in millimeter wave (mmWave) multiple-input multiple-output (MIMO) communication networks. However, perfect or nearly perfect channel state information (CSI) is fundamental in order to achieve their full potential. Traditionally, an RIS is fully passive without any baseband processing capabilities, which poses great challenges for CSI acquisition. Thus, we focus on the hybrid RIS architecture, where a small portion of RIS elements are active and able to processing the received pilot signals for estimating the corresponding channel. The channel estimation (CE) is done by resorting to off-the-grid compressive sensing technique, i.e., atomic norm minimization, for exacting channel parameters through two stages. Simulation results show that the proposed scheme outperforms the passive RIS CE under the same training overhead.