重庆邮电大学学报（自然科学版）2024，Vol.36Issue(2)：259-267,9.DOI:10.3979/j.issn.1673-825X.202212290381
MEC架构下基于DDPG的车联网任务卸载和资源分配
DDPG-based computation offloading and resource allocation in MEC-enabled internet of vehicles
摘要
Abstract
To alleviate the severe task processing delay caused by insufficient computing resources of individual vehicle in the MEC-enabled internet of vehicles,a dynamic joint computation offloading and resource allocation scheme was proposed.With the goal of minimizing the holistic task processing delay in the internet of vehicles,the problem of joint computation offloading and resource allocation was modeled as a Markov decision process(MDP),and then the problem was further solved using a deep deterministic policy gradient(DDPG)algorithm.The simulation results show that compared with the actor-critic(AC)and deep Q-network(DQN)algorithms,the proposed DDPG algorithm attains the holistic task process-ing delay minimum with superior convergence.关键词
车联网/移动边缘计算/马尔可夫决策过程/深度确定性策略梯度Key words
internet of vehicles/mobile edge computing/Markov decision process/deep deterministic strategy gradient分类
信息技术与安全科学引用本文复制引用
杨金松,孙三山,刘莉,熊有志,冯波涛,陆凌蓉..MEC架构下基于DDPG的车联网任务卸载和资源分配[J].重庆邮电大学学报（自然科学版）,2024,36(2):259-267,9.基金项目
中央高校科研经费项目(ZYGX2020ZB044) （ZYGX2020ZB044）
四川省自然科学基金项目(2022NSFSC0480) The Fundamental Research Funds for Central Universities(ZYGX2020ZB044) （2022NSFSC0480）
The Natural Science Foundation of Sichuan Province(2022NSFSC0480) （2022NSFSC0480）
