一种基于可移动天线和深度强化学习的无人机中继通信方案  

A UAV Relay Communication Scheme Based on Movable Antenna and Deep Reinforcement Learning

作　　者：郭天昊 尹瑞龙 李仙钟 闫远超 陈桢 GUO Tianhao;YIN Ruilong;LI Xianzhong;YAN Yuanchao;CHEN Zhen(College of Physical and Electronic Engineering,Shanxi University,Taiyuan 030006,China;Shanxi Key Laboratory of Wireless Communication and Detection,Taiyuan 030006,China)

机构地区：[1]山西大学物理电子工程学院,山西太原030006 [2]无线通信与检测山西省重点实验室,山西太原030006

出　　处：《移动通信》2025年第4期96-103,共8页Mobile Communications

基　　金：山西省基础研究计划资助项目(202303021212014);山西省回国留学人员科研资助项目(2023-005)。

摘　　要：在传统的UAV中继通信架构下,UAV及UE采用固定位置天线,这制约了通信系统对信道动态变化的自适应调节能力。为突破这一限制,文章为UAV及UE配备可移动天线,以提升通信系统对时变通信环境的适应能力。文章采用软演员-评论家算法,通过联合优化UAV的位置、UAV可移动天线的位置和UE可移动天线的位置,以最大化所有UE的最小通信速率。仿真结果表明,与采用固定位置天线的方案相比,文章所提方案将UE的最小通信速率提高了约16%。In the traditional unmanned aerial vehicle(UAV)relay communication architecture,UAVs and user equipment(UE)are equipped with fixed-position antennas,which constrains the adaptive ability of the communication system to dynamic channel variations.To overcome this limitation,movable antennas are equipped on the UAV and UEs to enhance the adaptability of the communication system to time-varying communication environments.This paper employs the soft actor-critic algorithm to jointly optimize the UAV's position,the UAV's movable antenna positions,and the positions of UE movable antennas,aiming to maximize the minimum communication rate among all UEs.The simulation results show that,compared with the fixedposition antenna scheme,the proposed scheme increases the minimum UE communication rate by approximately 16%.

关 键 词：中继通信 无人机 可移动天线 深度强化学习 

分 类 号：TN929.5[电子电信—通信与信息系统]