Deep Reinforcement Learning-Based URLLC-Aware Task Offloading in Collaborative Vehicular Networks

作     者：Chao Pan Zhao Wang Zhenyu Zhou Xincheng Ren Chao Pan;Zhao Wang;Zhenyu Zhou;Xincheng Ren

作者机构：Hebei Key Laboratory of Power Internet of Things TechnologyNorth China Electric Power UniversityBeijing102206Baoding071003HebeiChina Shaanxi Key Laboratory of Intelligent Processing for Big Energy DataYanan UniversityYanan 716000China 

出 版 物：《China Communications》 (中国通信（英文版）)

年 卷 期：2021年第18卷第7期

页      面：134-146页

核心收录：

学科分类：0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0810[工学-信息与通信工程] 081104[工学-模式识别与智能系统] 0839[工学-网络空间安全] 080204[工学-车辆工程] 0804[工学-仪器科学与技术] 080402[工学-测试计量技术及仪器] 0838[工学-公安技术] 0802[工学-机械工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This work was partially supported by the Open Funding of the Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data under Grant Number IPBED3 supported by the National Natural Science Foundation of China(NSFC)under Grant Number 61971189 supported by the Fundamental Research Funds for the Central Universities under Grant Number 2020MS001. 

主　　题：collaborative vehicular networks task of-floading URLLC awareness deep Q-learning 

摘      要：Collaborative vehicular networks is a key enabler to meet the stringent ultra-reliable and lowlatency communications(URLLC)requirements.A user vehicle(UV)dynamically optimizes task offloading by exploiting its collaborations with edge servers and vehicular fog servers(VFSs).However,the optimization of task offloading in highly dynamic collaborative vehicular networks faces several challenges such as URLLC guaranteeing,incomplete information,and dimensionality curse.In this paper,we first characterize URLLC in terms of queuing delay bound violation and high-order statistics of excess backlogs.Then,a Deep Reinforcement lEarning-based URLLCAware task offloading algorithM named DREAM is proposed to maximize the throughput of the UVs while satisfying the URLLC constraints in a besteffort way.Compared with existing task offloading algorithms,DREAM achieves superior performance in throughput,queuing delay,and URLLC.