无线电通信技术2024，Vol.50Issue(6)：1177-1183,7.DOI:10.3969/j.issn.1003-3114.2024.06.016
天地融合网络中基于深度强化学习的计算卸载算法研究
Research on DRL-based Computation Offloading Algorithm in Integrated Terrestrial-Satellite Networks
摘要
Abstract
With the rapid development of the Low Earth Orbit(LEO)satellite network and Mobile Edge Computing(MEC)tech-nology,by deploying MEC servers in LEO satellites,computation offloading services can be provided for remote areas where there is a lack of terrestrial MEC servers.However,as the number of ground users increases,the complexity of integrated terrestrial-satellite net-work computation offloading scenarios has grown significantly.Existing studies have difficulties dealing with scenarios characterized by high arrival rates and complex tasks.To solve this problem,a Deep Reinforcement Learning(DRL)-based Parallel Computation Offload-ing(DPCO)algorithm is proposed.This algorithm models the computation offloading problem with the optimization objective of minimi-zing the total offloading delay.It also considers the impact of Amdahl's law on computational performance and allocates computing re-sources of satellite MEC servers to enable multi-task parallel processing.Additionally,the DPCO algorithm transforms the model into a Markov Decision Process(MDP)and solves it using the Advantage Actor-Critic(A2C)algorithm.Finally,the performance of the DP-CO algorithm is verified by simulation.Simulation results show that the algorithm effectively addresses existing methods'deficiencies and provides valuable references for designing computation offloading algorithms in integrated terrestrial-satellite networks.关键词
计算卸载/移动边缘计算/天地融合网络/深度强化学习Key words
computation offloading/MEC/integrated terrestrial-satellite network/DRL分类
信息技术与安全科学引用本文复制引用
王从羽,罗志勇..天地融合网络中基于深度强化学习的计算卸载算法研究[J].无线电通信技术,2024,50(6):1177-1183,7.基金项目
国家重点研发计划(2023YFB2904701) （2023YFB2904701）
广东省基础与应用基础研究基金(2023B1515120093) （2023B1515120093）
广东省重点研发计划(2024B0101020006) （2024B0101020006）
深圳市重点项目(KJZD20230928112759002) National Key R&D Program of China(2023YFB2904701) （KJZD20230928112759002）
Guangdong Basic and Applied Basic Research Foundation(2023B1515120093) （2023B1515120093）
Key R&D Program of Guangdong Province(2024B0101020006) （2024B0101020006）
Shenzhen Key Project(KJZD20230928112759002) （KJZD20230928112759002）
