Dynamic Hierarchical Optimization for Train-to-Train Communication System
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 p...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
|
| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/13/1/50 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841549155623763968 |
|---|---|
| author | Haifeng Song Mingxuan Xu Yu Cheng Xiaoqing Zeng Hairong Dong |
| author_facet | Haifeng Song Mingxuan Xu Yu Cheng Xiaoqing Zeng Hairong Dong |
| author_sort | Haifeng Song |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-8650509384f541eb9a2ab47dd193549b |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-8650509384f541eb9a2ab47dd193549b2025-01-10T13:18:05ZengMDPI AGMathematics2227-73902024-12-011315010.3390/math13010050Dynamic Hierarchical Optimization for Train-to-Train Communication SystemHaifeng Song0Mingxuan Xu1Yu Cheng2Xiaoqing Zeng3Hairong Dong4School of Electronic and Information Engineering, Beihang University, Beijing 100191, ChinaSchool of Automation and Intelligence, Beijing Jiaotong University, Beijing 100044, ChinaInfrastructure Inspection Research Institute, China Academy of Railway Sciences Corporation Limited, Beijing 100081, ChinaThe Key Laboratory of Road and Traffic Engineering in Ministry of Education, Traffic School of Tongji University, Shanghai 200092, ChinaCollege of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, ChinaTo 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.https://www.mdpi.com/2227-7390/13/1/50T2T cooperative communicationcommunication path optimizationdynamic hierarchical algorithmstochastic network calculus |
| spellingShingle | Haifeng Song Mingxuan Xu Yu Cheng Xiaoqing Zeng Hairong Dong Dynamic Hierarchical Optimization for Train-to-Train Communication System Mathematics T2T cooperative communication communication path optimization dynamic hierarchical algorithm stochastic network calculus |
| title | Dynamic Hierarchical Optimization for Train-to-Train Communication System |
| title_full | Dynamic Hierarchical Optimization for Train-to-Train Communication System |
| title_fullStr | Dynamic Hierarchical Optimization for Train-to-Train Communication System |
| title_full_unstemmed | Dynamic Hierarchical Optimization for Train-to-Train Communication System |
| title_short | Dynamic Hierarchical Optimization for Train-to-Train Communication System |
| title_sort | dynamic hierarchical optimization for train to train communication system |
| topic | T2T cooperative communication communication path optimization dynamic hierarchical algorithm stochastic network calculus |
| url | https://www.mdpi.com/2227-7390/13/1/50 |
| work_keys_str_mv | AT haifengsong dynamichierarchicaloptimizationfortraintotraincommunicationsystem AT mingxuanxu dynamichierarchicaloptimizationfortraintotraincommunicationsystem AT yucheng dynamichierarchicaloptimizationfortraintotraincommunicationsystem AT xiaoqingzeng dynamichierarchicaloptimizationfortraintotraincommunicationsystem AT hairongdong dynamichierarchicaloptimizationfortraintotraincommunicationsystem |