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.

翻译：在双层速率分割多址接入（RSMA）等先进多用户系统中增强用户公平性是一项关键且具有挑战性的任务。本文提出了一种新颖的可移动天线（MA）方法来解决这一挑战。我们构建了一个最大最小公平性问题，通过联合优化波束成形矩阵、用户聚类、公共速率分配以及天线位置向量（APV），最大化作为公平性关键指标的最小用户速率。为解决这一非凸问题，我们开发了一种高效的双层迭代算法。外层循环利用动态邻域剪枝粒子群优化方法寻找高质量的APV，而内层循环则针对给定的APV优化其余变量。仿真结果验证了所提方法的有效性，表明该方案相比多种基准方案能带来显著的公平性增益。