Mobility-aware Clustering Routing Algorithm for Urban Rail Transit Ad Hoc Network

Abstract

With the development of urban rail transit systems, Communication-Based Train Control (CBTC) system choose to deploy Ad Hoc network alongside the track for train-to-trackside communication. However, due to the mobility of the train, how to efficiently send information to the train by Ad Hoc network still remains a challenge. Considering the dynamic characteristics of the train and multiple optimization objectives, we propose a mobility-aware multi-objective Deep Deterministic Policy Gradient (DDPG) algorithm for routing to optimize delay, throughput and energy consumption. We first set up the clustering routing model according to the dynamic routing scenario. To solve the problem of route selection, Markov decision process (MDP) models are constructed for intra-cluster optimization and inter-cluster optimization respectively, and train operating conditions are considered in inter-cluster MDP. Then we propose a multi-objective DDPG routing algorithm to get the optimal routing, where delay, throughput and energy consumption are designed as a three-dimensional vector. Simulation results indicate that our scheme optimizes multiple objectives in a balanced manner, and shows better performance compared with other schemes.