Cooperative and cognitive wireless networks for train control systems

With recent advances in wireless networks, communication-based train control (CBTC) has become a popular approach to ensure the safe and efficient operation of railway trains. The requirements for train–ground communication in CBTC systems are stringent. Most existing works about train–ground communication systems consider the infrastructure mode without train–train direct communications. Due to unreliable wireless communications and frequent handovers, existing CBTC systems can severely affect train control efficiency and performance, as well as the utility of railway. In this paper, with recent advances in cooperative and cognitive wireless networks, we propose a CBTC system to enable train–train direct communications. In addition, the proposed cooperative and cognitive CBTC system is optimized with the cognitive control method. Unlike the exiting works on cooperative and cognitive wireless networks, in this paper, train control performance in CBTC systems is explicitly used as the performance measure in the design. Reinforcement learning is applied to obtain the optimal handover decision and adaption policy of communication parameters. Simulation result shows that the performance of train control can be improved significantly in our proposed cooperative and cognitive CBTC system.