电子科技大学学报2026，Vol.55Issue(3)：350-360,11.DOI:10.12178/1001-0548.2025155
面向SAGIN场景的无人机缓存决策
Research on UAV cache decision in SAGIN scenario
摘要
Abstract
In the mobile edge computing scenario,the task cache delay becomes excessively long due to the rapid growth of data traffic.To address this issue,a cache scheme based on deep reinforcement learning algorithm in mobile edge computing scenarios is proposed.Firstly,under a 5G-based space-air-ground integrated network(SAGIN)architecture,a cache and communication model is established to minimize the content cache delay.Secondly,the SAC(soft actor critical)algorithm is used to interfere with the local minimum cache delay,and a new scheme is accepted with a certain probability,thereby achieving the global maximum cache hit rate.Finally,the above process is iterated repeatedly to obtain an optimal solution for the target problem,ensuring that the task files are pre-delayed in the optimal location.The simulation results show that under the SAGIN cooperation architecture,compared with the PPO(proximal policy optimization)scheme,the cache scheme can reduce the optimization transmission efficiency,reducing the cache delay by 5.30%,and improving the cache hit rate by 3.90%.关键词
移动边缘计算/深度强化学习/无人机/空天地一体化网络/缓存决策Key words
mobile edge computing/deep reinforcement learning/unmanned aerial vehicle/air-space-ground integration network/caching strategy分类
信息技术与安全科学引用本文复制引用
朱思峰,朱海,许浩,张青华,张宗辉,郝志鹏,鲍磊,乔蕊,陈国强,许蒙蒙..面向SAGIN场景的无人机缓存决策[J].电子科技大学学报,2026,55(3):350-360,11.基金项目
国家自然科学基金(62172457) （62172457）
天津市自然科学基金重点项目(22JCZDJC00600) （22JCZDJC00600）
河南省高校科技创新人才支持计划(23HASTIT029) （23HASTIT029）
河南省科技攻关项目(242102210027) （242102210027）
