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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:

27

期数:

2017年03期

页码:

85-90

栏目:

智能、算法、系统工程

出版日期:

2017-03-10

文章信息/Info

Title:

 Research on Technology of Channel Access Based on Q-Learning Algorithm for Vehicular Communication

文章编号:

1673-629X（2017)03-0085-06

作者:

 杜艾芊; 赵海涛; 刘南杰

南京邮电大学 通信与信息工程学院；南京邮电大学 网络基因工程研究所

Author(s):

 DU Ai-qian；ZHAO Hai-tao；LIU Nan-jie

关键词:

 ; 车载网络; BEB算法; 竞争窗口; Q学习算法; 分布式协调功能

Keywords:

 ; vehicular network; BEB algorithm; contention window; Q-Learning algorithm; DCF

分类号:

TP301

文献标志码:

A

摘要:

 针对基于IEEE 802.11p协议的车载网络MAC层DCF(分布式协调功能)信道接入方法存在数据包接收率低、时延高、可扩展性差等问题,提出一种基于Q学习的CW动态调整算法-QL-CWmin算法.区别于现有的BEB算法,通过利用Q学习,网络节点(Agent)能够不断地与周围环境进行交互学习,根据学习结果动态地调整竞争窗口(CW),使节点总能以最佳的CW(从周围环境中获得奖赏值最大时所选的CW大小)接入信道,以减少数据帧碰撞、降低端到端传输时延.仿真结果表明,采用QL-CWmin算法的通信节点能快速适应车联网的未知环境,数据包接收率和数据包传输时延得到了有效改善,同时该算法能为节点接入信道提供更高的公平性,适用于各种不同负载程度的网络环境.

Abstract:

 A Q-Learning based back-off algorithm is proposed because the traditional DCF approach used for IEEE 802. 11p MAC pro-tocol to access the channel has some problems of the low packet delivery rate,high delay and the poor scalability in VANETs. The pro-posed algorithm,which is quite different from the traditional BEB algorithm,is adopted by the nodes ( Agents) to interact with surround-ings continuously and learn from each other. The vehicle nodes adjust the size of CW ( Contention Window) dynamically according to the results learned from the surroundings so that the nodes can access the channel with the optimal CW eventually minimizing the packet col-lisions and end-to-end delay. The simulation results show that the communication nodes using the proposed algorithm can adapt to the unknown vehicular environment rapidly,and simultaneously the high packet delivery ratio,low end-to-end delay and high fairness can be achieved for vehicular network with various level load.

常用功能

更新日期/Last Update: 2017-05-12