AI-aided Traffic Control Scheme for M2M Communications in the Internet of Vehicles

被引:0
|
作者
Zhang, Haijun [1 ]
Jiang, Minghui [1 ]
Liu, Xiangnan [1 ]
Long, Keping [1 ]
Leung, Victor C. M. [2 ]
机构
[1] Univ Sci & Technol Beijing, Beijing, Peoples R China
[2] Univ British Columbia, Vancouver, BC, Canada
基金
国家重点研发计划; 北京市自然科学基金;
关键词
ACCESS;
D O I
暂无
中图分类号
TN [电子技术、通信技术];
学科分类号
0809 ;
摘要
Due to the rapid growth of data transmissions in internet of vehicles (IoV), finding schemes that can effectively alleviate access congestion has become an important issue. Recently, many traffic control schemes have been studied. Nevertheless, the dynamics of traffic and the heterogeneous requirements of different IoV applications are not considered in most existing studies, which is significant for the random access resource allocation. In this paper, we consider a hybrid traffic control scheme and use proximal policy optimization (PPO) method to tackle it. Firstly, IoV devices are divided into various classes based on delay characteristics. The target of maximizing the successful transmission of packets with the success rate constraint is established. Then, the optimization objective is transformed into a markov decision process (MDP) model. Finally, the access class barring (ACB) factors are obtained based on the PPO method to maximize the number of successful access devices. The performance of the proposal algorithm in respect of successful events and delay compared to existing schemes is verified by simulations.
引用
收藏
页码:5053 / 5057
页数:5
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