Multi-Agent Reinforcement Learning Enabled Spectrum Sharing for Vehicular Networks

被引：0

作者：

Wang W.-N. ^{[1
]}

Su J. ^{[1
]}

Chen Y. ^{[2
]}

Zhang J.-Z. ^{[2
]}

Tang Z. ^{[1
]}

机构：

[1] School of Computer and Software, Nanjing University of Information Science and Technology, Jiangsu, Nanjing

[2] The 63rd Research Institute, National University of Defense Technology, Jiangsu, Nanjing

来源：

Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2024年 / 52卷 / 05期

关键词：

Deep reinforcement learning; Distributed spectrum sharing; Multi agent; Vehicular network;

D O I：

10.12263/DZXB.20220320

中图分类号：

学科分类号：

摘要：

Aiming at the problem that it is difficult for base stations to collect and manage instantaneous channel state information in high dynamic vehicle networking environment, a spectrum allocation algorithm for vehicle networking based on multi-agent deep reinforcement learning is proposed. The algorithm aims to maximize the network throughput under the constraints of vehicle communication delay and reliability, and uses the learning algorithm to improve the spectrum and power allocation strategy. Firstly, the implicit cooperative agent is trained by improving DQN model and EXP3 strategy. Secondly, the nonstationary problem caused by multi-agent concurrent learning is solved by using hysteretic Q-learning and concurrent experience replay trajectory. The simulation results show that the average successful delivery rate of the payload of the proposed algorithm can reach 95.89%, which is 16.48% higher than the random baseline algorithm. It can quickly obtain the approximate optimal solution, and has significant advantages in reducing the signaling overhead of the Internet of vehicles communication system. © 2024 Chinese Institute of Electronics. All rights reserved.

引用

页码：1690 / 1699

页数：9

共 24 条

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