Relay Selection and Resource Allocation for Ad Hoc Networks-Assisted Train-to-Train Communications: A Federated Soft Actor-Critic Approach

• Published in: IEEE transactions on vehicular technology 2024-10, Vol.73 (10), p.15359-15371
• Main Authors: Li, Meng, Ma, Sixing, Si, Pengbo, Zhang, Haijun
• Format: Article
• Language: English
• Subjects: Ad hoc networks; ad-hoc network; Algorithms; Energy consumption; federated soft actor-critic (FeSAC) approach; Mobile ad hoc networks; Optimization; Packet transmission; Power control; Queueing; Relay; relay selection; Relays; Resource allocation; Resource management; Throughput; Train-to-Train (T2T) communications; Training; Transmission efficiency; Vehicular ad hoc networks; Wireless communications
• ISSN: 0018-9545; 1939-9359
• DOI: 10.1109/TVT.2024.3399079

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 T2T agent and accelerate model convergence, the FeSAC algorithm aggregates the network parameters by calculating the weighted mean value based on the reward of individual T2T agents. Evaluated by the simulation, the proposed FeSAC algorithm is more capable of increasing throughput and reducing energy consumption compared to other algorithms.