ScholarWorks@중앙대학교

초록

Rate-splitting multiple access (RSMA) and unmanned aerial vehicles (UAVs) are emerging as key technologies for enhancing connectivity and resource efficiency in future 6G networks. This study investigated a UAV-assisted RSMA downlink communication system and developed a novel framework that jointly optimizes the UAV’s trajectory, precoding matrix, and common rate to maximize the minimum achievable user rate. The proposed system was formulated as a Markov decision process (MDP) and solved using deep reinforcement learning (DRL), specifically using the proximal policy optimization (PPO) algorithm. This learning-based approach enables UAVs to adapt dynamically to the environment without relying on prior channel state information (CSI), allowing for efficient resource allocation and interference management in complex and dynamic wireless scenarios. Furthermore, a precoding design based on a uniform rectangular array (URA) was employed to enhance directional transmission and spatial multiplexing. In simulations, the proposed method significantly outperformed existing benchmarks, achieving an average minimum rate improvement of 23.31% over Deep Deterministic Policy Gradient (DDPG), 48.59% over Soft Actor–Critic (SAC), 50.87% over Trust Region Policy Optimization (TRPO), 63.36% over REINFORCE algorithm, 79.13% over Greedy algorithm, and 145.67% over Random strategies, respectively. These results confirm the potential of UAV-aided RSMA networks in next-generation wireless environments.

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