An Intelligent Congestion Control Strategy in Heterogeneous V2X Based on Deep Reinforcement Learning

Symmetry

Haoyu Li¹  Hui Wang²  Yuan Zhao² 

[1] Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650500, China;School of Computer Science and Engineering, Xi’an Technological University, Xi’an 710021, China;

关键词: heterogeneous V2X;    deep reinforcement learning (DRL);    intelligent congestion control;    congestion sensitive factor;    QoS;   

DOI  :  10.3390/sym14050947

来源: DOAJ

【 摘 要 】

High mobility and the complexity of mobile behavior are the main characteristics of nodes in Vehicle to Everything (V2X). Furthermore, these characteristics entail that resource deployment cannot effectively meet the demands of users for differentiated service quality. Due to this significance, the main objective of this study is to propose an intelligent congestion control strategy based on deep reinforcement learning (ICCDRL) in heterogeneous V2X, which can meet the diverse service needs of vehicles to some extent, so as to solve the problem of network congestion effectively. The proposal is implemented through three aspects: Firstly, the paper establishes a congestion control model based on DRL. Secondly, a large amount of QoS data is used as the training set to optimize the model. Finally, the congestion sensitivity factor is used to select the size of the congestion window for the next moment, resulting in an intelligent congestion control strategy based on QoS on-demand drive. For verification, a series of simulation experiments are designed on the ns-3 simulation platform. The results show that the proposed ICCDRL outperforms the traditional algorithm in terms of throughput, convergence, friendliness and fairness, and can effectively guarantee real-time, reliable information interaction in V2X.

【 授权许可】

Unknown