Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China.
Sensors (Basel). 2023 Jun 25;23(13):5883. doi: 10.3390/s23135883.
In the communication-based train control (CBTC) system, traditional modes such as LTE or WLAN in train-to-train (T2T) communication face the problem of a complex and costly deployment of base stations and ground core networks. Therefore, the multi-hop ad hoc network, which has the characteristics of being relatively flexible and cheap, is considered for CBTC. However, because of the high mobility of the train, it is likely to move out of the communication range of wayside nodes. Moreover, some wayside nodes are heavily congested, resulting in long packet queuing delays that cannot meet the transmission requirements. To solve these problems, in this paper, we investigate the next-hop relay selection problem in multi-hop ad hoc networks to minimize transmission time, enhance the network throughput, and ensure the channel quality. In addition, we propose a multiagent dueling deep Q learning (DQN) algorithm to optimize the delay and throughput of the entire link by selecting the next-hop relay node. The simulation results show that, compared with the existing routing algorithms, it has obvious improvement in the aspects of delay, throughput, and packet loss rate.
在基于通信的列车控制（CBTC）系统中，传统的 LTE 或 WLAN 等方式在车-车（T2T）通信中面临基站和地面核心网络部署复杂且成本高的问题。因此，多跳自组织网络因其相对灵活和廉价的特点而被应用于 CBTC。然而，由于列车的高移动性，列车很可能会移出路边节点的通信范围。此外，一些路边节点拥塞严重，导致分组排队延迟过长，无法满足传输要求。为了解决这些问题，本文研究了多跳自组织网络中的下一跳中继选择问题，以最小化传输时间，提高网络吞吐量，并确保信道质量。此外，我们提出了一种多智能体决斗深度 Q 学习（DQN）算法，通过选择下一跳中继节点来优化整个链路的延迟和吞吐量。仿真结果表明，与现有的路由算法相比，该算法在延迟、吞吐量和分组丢失率方面都有明显的改善。
