摘要
为实现车辆终端用户任务执行时延与处理速率、能耗的最佳均衡关系,针对车联网的边缘接入环境,提出了一种基于深度Q网络(DQN)的计算任务分发卸载算法。首先根据层次分析法对不同车辆终端的计算任务进行优先级划分,从而为计算任务处理速率赋予不同的权重建立关系模型;然后引入基于深度Q网络的边缘计算方法,以计算任务处理速率加权和为优化目标建立任务卸载模型;最后建立基于DQN的车辆终端自主最优任务卸载策略,最大化卸载决策制定模型的长期效用。仿真结果表明,相比Q学习算法,所提算法有效提高了任务执行效率。
In order to achieve the best balance between latency,computational rate and energy consumption,for a edge access network of IoV,a distribution offloading algorithm based on deep Q network(DQN)was considered.Firstly,these tasks of different vehicles were prioritized according to the analytic hierarchy process(AHP),so as to give different weights to the task processing rate to establish a relationship model.Secondly,by introducing edge computing based on DQN,the task offloading model was established by making weighted sum of task processing rate as optimization goal,which realized the long-term utility of strategies for offloading decisions.The performance evaluation results show that,compared with the Q-learning algorithm,the average task processing delay of the proposed method can effectively improve the task offload efficiency.
作者
赵海涛
张唐伟
陈跃
赵厚麟
朱洪波
ZHAO Haitao;ZHANG Tangwei;CHEN Yue;ZHAO Houlin;ZHU Hongbo(Ministry of Education Ubiquitous Network Health Service System Engineering Research Center,Nanjing 210003,China;Jiangsu Key Wireless Communication Laboratory,Nanjing 210003,China;College of Communication and Information Engineering,Nanjing University of Posts and Telecommunications,Nanjing 210003,China)
出处
《通信学报》
EI
CSCD
北大核心
2020年第10期172-178,共7页
Journal on Communications
基金
国家自然科学基金资助项目(No.61771252)
江苏省自然科学基金资助项目(No.BK20171444)
江苏省高等学校自然科学研究基金资助项目(No.18KJA510005)
江苏省科技成果转化专项基金资助项目(No.BA2019058)
江苏省“333高层次人才培养工程”基金资助项目(No.JSCX17_0224)。
