Enhancing User Fairness in Two-Layer RSMA: A Movable Antenna Approach

TL;DR

This paper introduces a movable antenna approach to improve user fairness in two-layer RSMA systems by jointly optimizing beamforming, user clustering, rate allocation, and antenna positions, validated through simulations.

Contribution

It presents a novel movable antenna method with a two-loop iterative algorithm to enhance fairness in two-layer RSMA, addressing a complex joint optimization problem.

Findings

Significant fairness improvements over benchmark schemes

Effective joint optimization of antenna positions and beamforming

Validated through comprehensive simulations

Abstract

Enhancing user fairness in advanced multi-user systems like two-layer rate-splitting multiple access (RSMA) is a critical yet challenging task. This letter proposes a novel movable antenna (MA) approach to address this challenge. We formulate a max-min fairness problem, maximizing the minimum user rate, a key metric for fairness, through the joint optimization of the beamforming matrices, user clustering, common rate allocation, and the antenna position vector (APV). To solve this non-convex problem, we develop an efficient two-loop iterative algorithm. The outer-loop leverages the dynamic neighborhood pruning particle swarm optimization method to find a high-quality APV, while the inner-loop optimizes the remaining variables for a given APV. Simulation results validate our approach, demonstrating that the proposed scheme yields significant fairness gains over various benchmark schemes.