Joint offloading decision and resource allocation in vehicular edge computing networks  

作　　者：Shumo Wang Xiaoqin Song Han Xu Tiecheng Song Guowei Zhang Yang Yang 

机构地区：[1]National Mobile Communications Research Laboratory,Southeast University,Nanjing,211189,China [2]College of Electronic and Information Engineering/College of Integrated Circuits,Nanjing University of Aeronautics and Astronautics,Nanjing,211106,China [3]School of Cyber Science of Engineering,Qufu Normal University,Qufu,273165,China [4]Terminus Group,Beijing,100027,China [5]Peng Cheng Laboratory,Shenzhen,518055,China

出　　处：《Digital Communications and Networks》2025年第1期71-82,共12页数字通信与网络(英文版)

基　　金：supported by Future Network Scientific Research Fund Project (FNSRFP-2021-ZD-4);National Natural Science Foundation of China (No.61991404,61902182);National Key Research and Development Program of China under Grant 2020YFB1600104;Key Research and Development Plan of Jiangsu Province under Grant BE2020084-2。

摘　　要：With the rapid development of Intelligent Transportation Systems(ITS),many new applications for Intelligent Connected Vehicles(ICVs)have sprung up.In order to tackle the conflict between delay-sensitive applications and resource-constrained vehicles,computation offloading paradigm that transfers computation tasks from ICVs to edge computing nodes has received extensive attention.However,the dynamic network conditions caused by the mobility of vehicles and the unbalanced computing load of edge nodes make ITS face challenges.In this paper,we propose a heterogeneous Vehicular Edge Computing(VEC)architecture with Task Vehicles(TaVs),Service Vehicles(SeVs)and Roadside Units(RSUs),and propose a distributed algorithm,namely PG-MRL,which jointly optimizes offloading decision and resource allocation.In the first stage,the offloading decisions of TaVs are obtained through a potential game.In the second stage,a multi-agent Deep Deterministic Policy Gradient(DDPG),one of deep reinforcement learning algorithms,with centralized training and distributed execution is proposed to optimize the real-time transmission power and subchannel selection.The simulation results show that the proposed PG-MRL algorithm has significant improvements over baseline algorithms in terms of system delay.

关 键 词：Computation offloading Resource allocation Vehicular edge computing Potential game Multi-agent deep deterministic policy gradient 

分 类 号：U495[交通运输工程—交通运输规划与管理] TN929.5[交通运输工程—道路与铁道工程]