重庆邮电大学学报（自然科学版）2025，Vol.37Issue(5)：668-676,9.DOI:10.3979/j.issn.1673-825X.202410140254
基于深度强化学习的无人机辅助RSMA中继通信技术
Unmanned aerial vehicle assisted RSMA relay communication technology based on deep reinforcement learning
樊自甫 1张珂瑞 1王正强1
作者信息
- 1. 重庆邮电大学通信与信息工程学院,重庆 400065
- 折叠
摘要
Abstract
To address the security challenges of relay communication in complex environments with potential eavesdroppers,this paper proposes a multi-UAV-assisted relay communication network that provides secure communication services for us-ers.A multi-agent deep reinforcement learning(MARL)algorithm based on the Q-mixing network(QMIX)is employed to jointly optimize UAV trajectories and power allocation.The goal is to guarantee the minimum transmission rate of low-securi-ty-sensitivity users(secondary users)while enhancing the communication security and data rate of high-security-sensitivity users(primary users).Simulation results demonstrate that,compared with the Double Deep Q-Network(Double DQN)and the Dueling Deep Q-Network(Dueling DQN),the proposed algorithm improves the cumulative reward by approximate-ly 15.5％and 1.26％,respectively.Moreover,the proposed rate-splitting multiple access(RSMA)technique significantly outperforms space-division multiple access(SDMA)and non-orthogonal multiple access(NOMA)in terms of overall sys-tem performance and information security.The proposed method provides an effective solution for achieving secure and effi-cient communication in multi-user wireless networks.关键词
速率分割多址接入/无线通信网络/深度强化学习/安全和速率Key words
rate-splitting multiple access(RSMA)/wireless communication networks/deep reinforcement learning(DRL)/security and rate分类
电子信息工程引用本文复制引用
樊自甫,张珂瑞,王正强..基于深度强化学习的无人机辅助RSMA中继通信技术[J].重庆邮电大学学报（自然科学版）,2025,37(5):668-676,9.
