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CLC number: U495
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2020-06-11
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Cooperative channel assignment for VANETs based on multiagent reinforcement learning
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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, 2020, 21(7): 1047-1058.
@article{title="Cooperative channel assignment for VANETs based on multiagent reinforcement learning",
author="Yun-peng Wang, Kun-xian Zheng, Da-xin Tian, Xu-ting Duan, Jian-shan Zhou",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="21",
number="7",
pages="1047-1058",
year="2020",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1900308"
}
%0 Journal Article
%T Cooperative channel assignment for VANETs based on multiagent reinforcement learning
%A Yun-peng Wang
%A Kun-xian Zheng
%A Da-xin Tian
%A Xu-ting Duan
%A Jian-shan Zhou
%J Frontiers of Information Technology & Electronic Engineering
%V 21
%N 7
%P 1047-1058
%@ 2095-9184
%D 2020
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1900308
TY - JOUR
T1 - Cooperative channel assignment for VANETs based on multiagent reinforcement learning
A1 - Yun-peng Wang
A1 - Kun-xian Zheng
A1 - Da-xin Tian
A1 - Xu-ting Duan
A1 - Jian-shan Zhou
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 21
IS - 7
SP - 1047
EP - 1058
%@ 2095-9184
Y1 - 2020
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1900308
Abstract: dynamic channel assignment (DCA) plays a key role in extending vehicular ad-hoc network capacity and mitigating congestion. However, channel assignment under vehicular direct communication scenarios faces mutual influence of large-scale nodes, the 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 nonlinear Q-function approximators, which facilitates the mapping of the continuously sensed input to the mixed policy output. Nodes are driven to locally share and incorporate their individual rewards such that they can optimize their policies in a distributed collaborative manner. Simulation results show that the proposed multiagent RL-CDCA can better reduce the one-hop packet delay by no less than 73.73%, improve the packet delivery ratio by no less than 12.66% on average in a highly dense situation, and improve the fairness of the global network resource allocation.
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