Task distribution offloading algorithm of vehicle edge network based on DQN

被引：0

作者：

Zhao H. ^{[1
,2
,3
]}

Zhang T. ^{[1
,2
,3
]}

Chen Y. ^{[1
,2
,3
]}

Zhao H. ^{[1
,2
,3
]}

Zhu H. ^{[1
,2
,3
]}

机构：

[1] Ministry of Education Ubiquitous Network Health Service System Engineering Research Center, Nanjing

[2] Jiangsu Key Wireless Communication Laboratory, Nanjing

[3] College of Communication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing

来源：

Tongxin Xuebao/Journal on Communications | 2020年 / 41卷 / 10期

基金：

中国国家自然科学基金;

关键词：

Computational offloading; Computational rate; DQN; IoV; MEC;

D O I：

10.11959/j.issn.1000-436x.2020160

中图分类号：

学科分类号：

摘要：

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. © 2020, Editorial Board of Journal on Communications. All right reserved.

引用

页码：172 / 178

页数：6

共 20 条

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