Stacked Intelligent Metasurface Assisted Multiuser Communications: From a Rate Fairness Perspective

TL;DR

This paper explores the use of stacked intelligent metasurfaces to improve rate fairness in multiuser wireless systems, proposing novel optimization algorithms that outperform existing methods and achieve near-digital beamforming performance with lower power.

Contribution

It introduces a multi-layered SIM structure and develops new ADMM and AO algorithms for rate fairness optimization, advancing electromagnetic control and signal processing capabilities.

Findings

Proposed algorithms outperform existing methods in rate fairness and sum-rate.

SIM achieves digital-beamforming-like performance with lower power consumption.

Algorithms demonstrate effective convergence and robustness in simulations.

Abstract

Stacked intelligent metasurface (SIM) extends the concept of single-layer reconfigurable holographic surfaces (RHS) by incorporating a multi-layered structure, thereby providing enhanced control over electromagnetic wave propagation and improved signal processing capabilities. This study investigates the potential of SIM in enhancing the rate fairness in multiuser downlink systems by addressing two key optimization problems: maximizing the minimum rate (MR) and maximizing the geometric mean of rates (GMR). {The former strives to enhance the minimum user rate, thereby ensuring fairness among users, while the latter relaxes fairness requirements to strike a better trade-off between user fairness and system sum-rate (SR).} For the MR maximization, we adopt a consensus alternating direction method of multipliers (ADMM)-based approach, which decomposes the approximated problem into sub-problems with closed-form solutions. {For GMR maximization, we develop an alternating optimization (AO)-based algorithm that also yields closed-form solutions and can be seamlessly adapted for SR maximization. Numerical results validate the effectiveness and convergence of the proposed algorithms.} Comparative evaluations show that MR maximization ensures near-perfect fairness, while GMR maximization balances fairness and system SR. Furthermore, the two proposed algorithms respectively outperform existing related works in terms of MR and SR performance. Lastly, SIM with lower power consumption achieves performance comparable to that of multi-antenna digital beamforming.