Achievable Rate Optimization for Large Stacked Intelligent Metasurfaces Based on Statistical CSI

TL;DR

This paper explores optimizing the achievable rate of large stacked intelligent metasurfaces for multiuser MIMO systems using statistical CSI, proposing an alternating optimization method to enhance performance while reducing overhead.

Contribution

It introduces a novel rate maximization approach for large SIMs based on statistical CSI, addressing challenges of overhead and scalability in multiuser beamforming.

Findings

AO algorithm improves achievable rate with large SIMs

Statistical CSI reduces overhead compared to instantaneous CSI

Large SIMs enhance multiuser MIMO performance

Abstract

Stacked intelligent metasurface (SIM) is an emerging design that consists of multiple layers of metasurfaces. A SIM enables holographic multiple-input multiple-output (HMIMO) precoding in the wave domain, which results in the reduction of energy consumption and hardware cost. On the ground of multiuser beamforming, this letter focuses on the downlink achievable rate and its maximization. Contrary to previous works on multiuser SIM, we consider statistical channel state information (CSI) as opposed to instantaneous CSI to overcome challenges such as large overhead. Also, we examine the performance of large surfaces. We apply an alternating optimization (AO) algorithm regarding the phases of the SIM and the allocated transmit power. Simulations illustrate the performance of the considered large SIM-assisted design as well as the comparison between different CSI considerations.