Train-Centric CBTC Meets Age of Information in Train-to-Train Communications

Quality of service (QoS) guarantee is critical in urban rail transit. In this paper, the train-centric communication-based train control (CBTC) systems through train-to-train (T2T) wireless communication is introduced based on the modification of LTE vehicle-to-everything (LTE-V2X). To be specific, a novel train-centric CBTC systems is established based on T2T wireless communication where distributed sensing-based semi-persistent scheduling (DS-SPS) is served as the resource allocation scheme in the T2T scenario. The quantized age of information (AoI) is used as an integrated system QoS indicator of the CBTC wireless communication systems in urban rail transit. Machine learning techniques especially Q-learning is further utilized to improve system AoI performance. Simulation results show that the proposed LTE-T2T based wireless communication systems in train-centric CBTC with Q-learning can achieve improved system AoI and peak AoI performance compared with fixed SPS policy. Furthermore, the system performance of the designed LTE-T2T based wireless communication systems in train-centric CBTC with Q-learning is shown to be better than traditional LTE-M and WLAN based wireless communication systems.