可重构智能超表面辅助的非地面网络安全传输与轨迹优化  

Joint Secure Transmission and Trajectory Optimization for Reconfigurable Intelligent Surface-aided Non-Terrestrial Networks

作　　者：徐可馨 隆克平[1] 陆阳 张海君 XU Kexin;LONG Keping;LU Yang;ZHANG Haijun(School of Computer and Communication Engineering,University of Science and Technology Beijing,Beijing 100083,China;China Electric Power Research Institute Co.Ltd.,Beijing 102209,China)

机构地区：[1]北京科技大学计算机与通信工程学院,北京100083 [2]中国电力科学研究院,北京102209

出　　处：《电子与信息学报》2025年第2期296-304,共9页Journal of Electronics & Information Technology

基　　金：国家自然科学基金(U2441227,U22B2003);国防基础科研计划(JCKY2022110C010);中央高校基本科研业务费专项资金(FRF-TP-22-002C2);通信抗干扰全国重点实验室开放课题(IFN20230201)。

摘　　要：由于卫星与地面用户之间的直连受限于覆盖范围和链路质量以及非地面网络存在窃听威胁等问题,该文考虑一个无人机中继的非地面网络安全传输系统,引入可重构智能超表面(RIS),提高合法用户信号质量。同时为了兼顾系统高传输速率和高安全需求,该文设计卫星到无人机的传输速率与地面合法用户的安全速率的加权和作为系统效用,并以此作为优化目标,进而提出一种基于双层双延迟深度确定性策略梯度(TTD3)的联合卫星与无人机波束成形、RIS相移矩阵以及无人机轨迹优化方法,通过采用双层深度强化学习结构解耦波束成形和轨迹优化两个子问题,实现系统效用最大化。仿真结果验证了所提方法在动态非地面网络环境下的有效性,同时在高安全需求下,通过对比不同算法、不同配置方案以及不同RIS元件数量下的仿真结果,证明了该文所提方法能够提升系统安全传输性能。Objective The proliferation of technologies such as the Internet of Things,smart cities,and next-generation mobile communications has made Non-Terrestrial Networks(NTNs)increasingly important for global communication.Future communication systems are expected to rely heavily on NTNs to provide seamless global coverage and efficient data transmission.However,current NTNs face challenges,including limited coverage and link quality in direct satellite-to-ground user connections,as well as eavesdropping threats.To address these challenges,a system integrating Reconfigurable Intelligent Surfaces(RIS)with a twin-layer Deep Reinforcement Learning(DRL)algorithm is proposed.This approach aims to satisfy the system’s requirements for high transmission rates and enhanced security,improving the signal strength for legitimate users while facilitating real-time updates and optimization of channel state information in NTNs.Methods First,an RIS-aided downlink NTNs system using an Unmanned Aerial Vehicle(UAV)as a relay is established.To balance the system’s transmission rate and security requirements,the weighted sum of the satellite-to-UAV transmission rate and the secure rate of the legitimate ground user is designed as the system utility,which serves as the optimization objective.A joint optimization method based on the Twin-Twin Delayed Deep Deterministic Policy Gradient(TTD3)algorithm is then proposed.This method jointly optimizes satellite and UAV beamforming,the RIS phase shift matrix,and UAV trajectory.The algorithm divides the optimization problem into two layers for solution.The first-layer DRL optimizes satellite and UAV beamforming,as well as the RIS phase shift matrix.The second-layer DRL optimizes the UAV’s trajectory based on its position,user mobility,and channel state information.The twin DRL shares the same reward function,guiding the agents in each layer to adjust their actions and explore optimal strategies,ultimately enhancing the system’s utility.Results and Discussions(1)Compared to the Deep Determin

关 键 词：可重构智能超表面 非地面网络 深度强化学习 安全传输 

分 类 号：TN929.5[电子电信—通信与信息系统]