在CBTC系统中,为特设网络辅助的火车对火车通信进行下一跳中继选择
Sixing Ma1, Meng Li1, Ruizhe Yang1
1Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China.
Sensors (Basel, Switzerland)
|July 14, 2023
概括
本研究介绍了基于通信的列车控制系统中列车对列车通信的多代理对决深度Q学习 (DQN) 算法. 新方法优化了下一跳中继选择,以减少延迟和提高吞吐量,克服传统网络的局限性.
科学领域:
- 铁路工程 铁路工程是指铁路工程.
- 无线通信网络 无线通信网络
- 交通运输中的人工智能
背景情况:
- 在基于通信的火车控制系统 (CBTC) 中,传统的火车对火车通信 (例如LTE,WLAN) 需要复杂而昂贵的基础设施.
- 多节点特设网络为CBTC提供了一种灵活且具有成本效益的替代方案.
- 特别网络中的高列车流动性和节点拥堵导致通信范围问题和显著的数据包队列延迟,阻碍实时传输需求.
研究的目的:
- 为了应对CBTC的多节点特设网络中有限的通信范围和高数据包延迟的挑战.
- 调查和优化下一个跳转中继选择过程.
- 为了最大限度地减少传输时间,提高网络吞吐量,并确保可靠的频道质量.
主要方法:
- 提出了一种新的多代理对决深度Q学习 (DQN) 算法.
- 该算法专注于优化选择下一个跳转中继节点.
- 通过对现有的路由算法进行模拟来评估算法的性能.
主要成果:
- 拟议的多代理对决DQN算法在关键性能指标中显示出显著的改进.
- 实现了显著的数据包延迟和数据包丢失率的减少.
- 与传统路由方法相比,展示了增强的整体网络吞吐量.
结论:
- 多代理对决的DQN算法有效地优化了CBTC在多跳特设网络中的下一跳继电器选择.
- 这种方法成功地减轻了与火车流动性和网络拥堵有关的问题.
- 这些发现表明,对于可靠和高效的列车通信系统来说,这是一个有前途的解决方案.
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