Edge Intelligence and Ad Hoc Network-Empowered Resource Allocation for CBTC System

With the growing demand for various applications in intelligent rail transit, the burden of information transmission is aggravated. Meanwhile, high-mobility trains, time-varying channels and limited package transmission delays bring great challenges to the quality of packet transmission in train-to-train (T2T) communications. In this paper, to guarantee the transmission quality and reduce the deployment cost, wireless ad hoc network as a novel technology is applied to the communications-based train control (CBTC) system. In order to further improve the transmission efficiency of multi-hop ad hoc networks, a federated soft actor-critic (FeSAC) approach is proposed for joint optimization of relay selection, subchannel allocation and power control. The goal of the FeSAC algorithm training is to make the throughput of T2T links as large as possible with less energy consumption, while ensuring the link reliability and queuing delay requirements of T2T communications. To synthesize the training results of each agents and accelerate model convergence, the FeSAC algorithm aggregates the network parameters by calculating the weighted mean value based on the reward of individual edge intelligent agents. The training tasks are offloaded to edge intelligent nodes, which makes parameter aggregation more easily through interconnection. Evaluated by the simulation, the proposed FeSAC algorithm is more capable of increasing throughput and reducing energy consumption compared to other algorithms.