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

2026-03-07

AI Summary

This work addresses the issue of insufficient user fairness in two-layer rate-splitting multiple access (RSMA) systems by introducing movable antennas for the first time. A novel joint optimization framework is proposed to maximize the minimum user rate through the coordinated design of beamforming, user clustering, common rate allocation, and antenna placement. To solve this challenging problem, a two-layer iterative algorithm is developed: the outer layer employs a dynamic neighborhood-pruning particle swarm optimization to efficiently search for optimal antenna positions, while the inner layer optimizes communication parameters under fixed antenna locations. Simulation results demonstrate that the proposed scheme significantly outperforms existing benchmark methods, achieving substantial gains in both the worst-user rate and overall system fairness.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Multiagent Systems: Mechanism Design

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• user fairness
• rate-splitting multiple access
• movable antenna
• max-min fairness
• multi-user systems

Innovation

• Movable Antenna
• Two-Layer RSMA
• Max-Min Fairness
• Joint Optimization
• Particle Swarm Optimization