본문 바로가기
  • Home

Transmission Control for User Fairness in UAV-Assisted RSMA Networks

  • Journal of Internet of Things and Convergence
  • Abbr : JKIOTS
  • 2025, 11(5), 22
  • Publisher : The Korea Internet of Things Society
  • Research Area : Engineering > Computer Science > Internet Information Processing
  • Received : September 2, 2025
  • Accepted : October 17, 2025
  • Published : October 31, 2025

NOH WONJONG 1

1한림대학교

Accredited

ABSTRACT

Rate-Splitting Multiple Access (RSMA) and Unmanned Aerial Vehicles (UAVs) have emerged as key technologies for enhancing connectivity and resource efficiency in future 6G networks. In this paper, we propose an algorithm that maximizes user fairness (or equivalently maximizes the minimum user data rate) by jointly optimizing the UAV trajectory, beamforming, and common message transmission rate in a UAV-assisted RSMA network. To achieve this, we formulate the problem using a Markov Decision Process (MDP) framework and propose a Proximal Policy Optimization (PPO)-based Deep Reinforcement Learning (DRL) algorithm to solve it. The proposed PPO algorithm enables efficient interference management and resource allocation even in dynamically changing environments, without relying on accurate Channel State Information (CSI). Simulation results demonstrate that the proposed method significantly outperforms existing approaches such as Deep Deterministic Policy Gradient (DDPG), Soft Actor-Critic (SAC), Trust Region Policy Optimization (TRPO), REINFORCE, Greedy, and Random schemes in terms of the minimum user data rate. These results confirm that the proposed system can be effectively utilized in UAV-assisted next-generation wireless networks.

Citation status

* References for papers published after 2024 are currently being built.