通信学报2025，Vol.46Issue(4)：272-281,10.DOI:10.11959/j.issn.1000-436x.2025060
基于深度强化学习的移动边缘计算安全传输策略研究
Research on secure transport strategy of mobile edge computing based on deep reinforcement learning
摘要
Abstract
In mobile edge computing,the process of task unloading will face security problems such as information leak-age and eavesdropping.To improve the unloading efficiency of mobile edge computing system,the physical layer secu-rity transmission strategy of mobile edge computing was proposed.Firstly,the mobile edge computing system based on unmanned aerial vehicle(UAV)was studied,which was composed of I user devices,M legal UAV(L-UAV)and N eaves-dropping UAV(E-UAV).Secondly,while ensuring the unloading of L-UAV within a specified period,the multi-agent depth deterministic policy gradient(Attention-MADDPG)algorithm with the addition of attention mechanism was ad-opted to solve and optimize the problem with the aim of maximizing the safety unloading efficiency of the communica-tion system.Finally,under the premise of ensuring uninstallation,the user's confidential information was not eaves-dropped by the eavesdropper,and the secure computing efficiency was maximized to ensure the overall security of the system.Simulation results show that compared with other benchmark algorithms,the proposed algorithm has better per-formance in terms of secure transmission efficiency.关键词
移动边缘计算/物理层安全/深度强化学习/无人机辅助卸载Key words
mobile edge computing/physical layer security/deep reinforcement learning/UAV assisted offloading分类
信息技术与安全科学引用本文复制引用
王义君,李嘉欣,闫志颖,吕婧莹,钱志鸿..基于深度强化学习的移动边缘计算安全传输策略研究[J].通信学报,2025,46(4):272-281,10.基金项目
国家自然科学基金资助项目(No.61540022) （No.61540022）
吉林省科技厅重点研发基金资助项目(No.20230203091SF) The National Natural Science Foundation of China(No.61540022),The Jilin Province Science and Technology Department Key Research and Development Project(No.20230203091SF) （No.20230203091SF）
