Dynamic Hierarchical Optimization for Train-to-Train Communication System

Haifeng Song (), Mingxuan Xu, Yu Cheng, Xiaoqing Zeng () and Hairong Dong
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Haifeng Song: School of Electronic and Information Engineering, Beihang University, Beijing 100191, China
Mingxuan Xu: School of Automation and Intelligence, Beijing Jiaotong University, Beijing 100044, China
Yu Cheng: Infrastructure Inspection Research Institute, China Academy of Railway Sciences Corporation Limited, Beijing 100081, China
Xiaoqing Zeng: The Key Laboratory of Road and Traffic Engineering in Ministry of Education, Traffic School of Tongji University, Shanghai 200092, China
Hairong Dong: College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China

Mathematics, 2024, vol. 13, issue 1, 1-21

Abstract: To enhance the operational efficiency of high-speed trains (HSTs), Train-to-Train (T2T) communication has received considerable attention. This paper introduces a T2T cooperative communication model that allows direct information exchange between HSTs, enhancing communication efficiency and system performance. The model incorporates a mix of dynamic and static nodes, and within this framework, we have developed a novel Dynamic Hierarchical Algorithm (DHA) to optimize communication paths. The DHA combines the stability of traditional algorithms with the flexibility of machine learning to adapt to changing network topologies. Furthermore, a communication link quality assessment function is proposed based on stochastic network calculus, which accounts for channel randomness, allowing for a more precise adaptation to the actual channel environment. Simulation results demonstrate that DHA has superior performance in terms of optimization time and effect, particularly in large-scale and highly dynamic network environments. The algorithm’s effectiveness is validated through comparative analysis with traditional and machine learning-based approaches, showing significant improvements in optimization efficiency as the network size and dynamics increase.

Keywords: T2T cooperative communication; communication path optimization; dynamic hierarchical algorithm; stochastic network calculus (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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