Achievable Rate Optimization for Stacked Intelligent Metasurface-Assisted Holographic MIMO Communications

TL;DR

This paper explores the use of stacked intelligent metasurfaces in holographic MIMO systems to optimize achievable rates through a joint iterative optimization of phase shifts and signal covariance, demonstrating significant performance gains.

Contribution

It introduces a novel joint optimization framework for SIM-assisted HMIMO, including an iterative algorithm with guaranteed convergence, outperforming single-layer metasurface systems.

Findings

Achieves higher rates than single-RIS systems.

Proposes an efficient iterative optimization algorithm.

Matches the performance of AO benchmarks with fewer iterations.

Abstract

Stacked intelligent metasurfaces (SIM) is a revolutionary technology, which can outperform its single-layer counterparts by performing advanced signal processing relying on wave propagation. In this work, we exploit SIM to enable transmit precoding and receiver combining in holographic multiple-input multiple-output (HMIMO) communications, and we study the achievable rate by formulating a joint optimization problem of the SIM phase shifts at both sides of the transceiver and the covariance matrix of the transmitted signal. Notably, we propose its solution by means of an iterative optimization algorithm that relies on the projected gradient method, and accounts for all optimization parameters simultaneously. We also obtain the step size guaranteeing the convergence of the proposed algorithm. Simulation results provide fundamental insights such the performance improvements compared to the single-RIS counterpart and conventional MIMO system. Remarkably, the proposed algorithm results in the same achievable rate as the alternating optimization (AO) benchmark but with a less number of iterations.