数字孪生辅助下基于D3QN的车载网络协同卸载算法
D3QN-based collaborative offloading algorithm for vehicular networks assisted by digital twins
摘要
Abstract
To address the challenges of highly dynamic topologies,task diversity,and low-latency constraints in vehicu-lar edge computing,a D3QN-based collaborative offloading algorithm assisted by digital twin technology was proposed.Firstly,a digital Siamese network was constructed to realize the dynamic modeling of traffic state,which integrated ve-hicle spatio-temporal and resource information,was different from the traditional static clustering strategy,improved the stability of collaborative clustering and reduced the strategy search space.Next,based on task decomposability,two types of task models were established,and a hybrid offloading strategy was devised to accurately adapt to dynamic real-world demands.Furthermore,a D3QN-based collaborative offloading algorithm for vehicle clusters was developed,uti-lizing a dual-network architecture to decouple action and target evaluations,thereby suppressing Q-value bias,accelerat-ing policy convergence,and achieving a balance between utility and latency.The simulation results demonstrate that the proposed scheme can significantly reduce task processing latency in high-dynamic and high-load scenarios,and achieves an average system utility improvement of 5.32%,8.54%,1.47%,11.2%,68.51%,and 103.15%compared to the other six baseline algorithms,respectively.关键词
车辆边缘计算/数字孪生/车辆集群/深度强化学习/任务多样性Key words
vehicular edge computing/digital twin/vehicle cluster/deep reinforcement learning/task diversity分类
信息技术与安全科学引用本文复制引用
陈赓,宋政翰,夏聪慧,曾庆田..数字孪生辅助下基于D3QN的车载网络协同卸载算法[J].通信学报,2025,46(8):90-104,15.基金项目
国家自然科学基金资助项目(No.61701284) （No.61701284）
山东省自然科学基金资助项目(No.ZR2022MF226) （No.ZR2022MF226）
山东科技大学青年教师人才培养计划基金资助项目(No.BJ20221101) （No.BJ20221101）
青岛市应用基础研究计划基金资助项目(No.19-6-2-1-cg) （No.19-6-2-1-cg）
山东科技大学菁英计划基金资助项目(No.skr21-3-B-048) （No.skr21-3-B-048）
山东省泰山学者计划基金资助项目(No.tstp20250506)The National Natural Science Foundation of China(No.61701284),The Natural Science Foundation of Shan-dong Province(No.ZR2022MF226),The Talented Young Teachers Training Program of Shandong University of Science and Techno-logy(No.BJ20221101),The Innovative Research Foundation of Qingdao(No.19-6-2-1-cg),The Elite Plan Project of Shandong Uni-versity of Science and Technology(No.skr21-3-B-048),The Taishan Scholar Program of Shandong Province(No.tstp20250506) （No.tstp20250506）
