电讯技术2025，Vol.65Issue(4)：503-510,8.DOI:10.20079/j.issn.1001-893x.240110001
空天地边缘计算网络任务卸载策略
Task Offloading Strategies for Space-Air-Ground Edge Computing Networks
摘要
Abstract
In the space-air-ground integrated edge computing network,a large number of computational tasks can lead to overloading of edge servers,which increases the completion time and energy consumption of user tasks.To solve the problem,a three-tier collaborative task offloading and resource allocation scheme based on deep reinforcement learning is proposed,which creates a task overhead function with task completion time and user energy consumption,and jointly optimizes the user offloading decision,user transmission power,subcarrier allocation and computational resource allocation under the constraints of computational resources.First,the Lagrange multiplier method is used to optimize the computational resource allocation.Secondly,deep reinforcement learning is used to solve the offloading decision,user transmission power and subcarrier allocation,and finally the optimized solution is obtained by an alternating iteration method.The simulation results show that,compared with that of Deep Q-learning Network(DQN),Double DQN(DDQN)and Deep Deterministic Policy Gradient(DDPG),the mission overhead of the proposed scheme declines by approximately 19%,10%and 13%.关键词
空天地一体化网络/移动边缘计算/计算卸载/资源分配/深度强化学习Key words
space-air-ground intergated network/mobile edge computing/computational offloading/resource allocation/deep reinforcement learning分类
信息技术与安全科学引用本文复制引用
余翔,曲原宇,杨路..空天地边缘计算网络任务卸载策略[J].电讯技术,2025,65(4):503-510,8.基金项目
重庆市自然科学基金创新发展联合基金(市教委)项目(CSTB2023NSCQ-LZX0076) （市教委）
