基于联合多代理深度强化学习的车辆网络中的通信资源分配方法
Qingli Liu1,2, Yongjie Ma3,4
1Key Laboratory of Communication and Network, Dalian University, Dalian, 116622, China.
Scientific reports
|August 22, 2025
概括
本研究介绍了车辆网络的联合多代理深度强化学习方法. 它提高了动态车辆到一切通信的频谱效率和传输成功率.
科学领域:
- 车辆网络
- 通信系统
- 机器学习
背景情况:
- 由于缺乏全球优化和对动态环境的缓慢响应,车辆网络中的传统资源配置存在低光谱效率.
- 车辆与基础设施 (V2I) 和车辆与车辆 (V2V) 之间的频谱资源共享对有效的资源管理构成重大挑战.
研究的目的:
- 提出使用联合多代理深度强化学习的车辆网络新型资源分配方法.
- 在动态车辆通信场景中提高系统光谱效率,V2V传输成功率和V2I链路容量.
主要方法:
- 将异步联合学习 (AFL) 与多代理深度决定性政策梯度 (MADDPG) 融合为协同资源分配.
- 车辆作为优化频谱访问,功率控制和基于本地频道状态的带宽分配的代理.
- 异步联合架构可实现独立的模型参数上传,基于频道质量的动态重量调整和全球模型优化.
主要成果:
- 与现有算法相比,系统的光谱效率平均提高了19.1%.
- 将V2V链接的平均传输成功率提高了9.3%.
- 将V2I连接的平均总容量提高了16.1%.
结论:
- 拟议的联合多代理深度强化学习方法有效优化车辆网络中的资源配置.
- 这种方法显著改善了关键性能指标,证明了其优于传统和其他先进的算法.
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