Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

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

Ji Luo, Yaxuan Chen, Guangchi Zhang, Miao Cui, Hao Fu, Changsheng You

TL;DR

A max-min fairness problem is formulated, 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 to solve this non-convex problem.

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.

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

TL;DR

A max-min fairness problem is formulated, 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 to solve this non-convex problem.

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.
Paper Structure (10 sections, 21 equations, 2 figures, 2 algorithms)

This paper contains 10 sections, 21 equations, 2 figures, 2 algorithms.

Table of Contents

  1. Introduction
  2. System Model and Problem Formulation
  3. Proposed Algorithm for Problem \ref{['overallproblem']}
  4. Outer-Loop Optimization
  5. Inner-Loop Optimization
  6. User Clustering Optimization
  7. Joint Beamforming and Rate Allocation Optimization
  8. Overall Algorithm
  9. Simulation Results
  10. Conclusion

Figures (2)

  • Figure 1: An MA-assisted two-layer RSMA system.
  • Figure 2: Max-min rate versus (a) number of users, with $N_T=4, P_{\text{max}}=30$ dBm; (b) transmit power, with $N_T=4, K=6$; (c) number of antennas, with $K=6, P_{\text{max}}=30$ dBm; and (d) maximum user distance, with $N_T=4, K=6, P_{\text{max}}=30$ dBm. All subfigures share the same legend.