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Frontiers of Information Technology & Electronic Engineering  1998 Vol.-1 No.-1 P.

10.1631/FITEE.1900308


Cooperative channel assignment for VANETs based on multiagent reinforcement learning


Author(s):  Yun-peng WANG, Kun-xian ZHENG, Da-xin TIAN, Xu-ting DUAN, Jian-shan ZHOU

Affiliation(s):  Beijing Advanced Innovation Center for Big Data and Brain Computing, School of Transportation Science and Engineering, Beihang University, Beijing 100191, China

Corresponding email(s):   ypwang@buaa.edu.cn, zhengkunxian@buaa.edu.cn, dtian@buaa.edu.cn, duanxuting@buaa.edu.cn

Key Words:  Vehicular ad hoc networks, Reinforcement learning, Dynamic channel assignment, Multichannel


Yun-peng WANG, Kun-xian ZHENG, Da-xin TIAN, Xu-ting DUAN, Jian-shan ZHOU. Cooperative channel assignment for VANETs based on multiagent reinforcement learning[J]. Frontiers of Information Technology & Electronic Engineering, 1998, -1(-1): .

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Abstract: 
dynamic channel assignment (DCA) plays a key role in extending vehicular ad hoc network (VANET) capacity and mitigating congestion. However, channel assignment under a vehicular direct communication scenario faces the mutual influence of large-scale nodes, lack of centralized coordination, unknown global state information, and other challenges. To solve this problem, a multiagent reinforcement learning (RL)-based cooperative DCA (RL-CDCA) mechanism is proposed. Specifically, each vehicular node can successfully learn the proper strategies of channel selection and backoff adaptation from the real-time channel state information (CSI) using two cooperative RL models. In addition, neural networks are constructed as the nonlinear Q-function approximators, which facilitate the mapping of the continuously sensed input to the mixed policies output. The nodes are also driven to locally share as well as incorporate their individual rewards such that they are enabled to optimize their policies in a distributed collaborative manner. Simulation results show that the proposed multi-agent RL-CDCA can better reduce the one-hop packet delay by ≥ 73.73% , improve the packet delivery ratio on average by ≥12.66% in a highly dense situation, and guarantee better fairness of the global vehicles’ performances when compared with four other existing mechanisms.

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