计算机应用与软件2025，Vol.42Issue(10)：306-312,7.DOI:10.3969/j.issn.1000-386x.2025.10.041
超密集网络环境中移动边缘计算任务卸载的深度强化学习算法
MOBILE EDGE COMPUTING TASK OFFLOADING ALGORITHM BASED ON DEEP REINFORCEMENT LEARNING IN ULTRA-DENSE NETWORK ENVIRONMENT
摘要
Abstract
To solve the problem of too static scenarios caused by ignoring the time-varying characteristics of communication networks and user mobility in the research of mobile edge computing task offloading,this paper considers an edge computing task offloading scenario in an ultra dense network environment with multiple base stations,which provides mobile users with real-time task offloading decisions without any prior information.Combined with the strong environment interaction ability of reinforcement learning,the problem was described as a Markov decision process,and the state and action spaces were redefined.A binary online task offloading algorithm based on priority sampling in dual deep Q network was proposed,and the CPU frequency of the device was optimized.The effectiveness of the proposed algorithm was verified by simulation experiments.关键词
任务卸载/边缘计算/深度强化学习/超密集网络/马尔可夫决策Key words
Task offload/Edge computing/Deep reinforcement learning/Ultra-dense network/Markov decision分类
计算机与自动化引用本文复制引用
张茜,戚续博,张聪,崔勇,王洪格..超密集网络环境中移动边缘计算任务卸载的深度强化学习算法[J].计算机应用与软件,2025,42(10):306-312,7.基金项目
国家自然科学基金项目(61902369) （61902369）
河南省科技攻关计划项目(222102210281,212102210409) （222102210281,212102210409）
中原工学院优势学科实力提升计划项目. （）
