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

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-...

Full description

Saved in:
Bibliographic Details
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
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2024.3399079