Design and Optimization of Adaptive Cooperative MAC Protocol With Priority Scheduling for Train-to-Train Communications
With the advancement of urbanization, communication-based train control (CBTC) systems for urban rail transit and train-to-train (T2T) communication have garnered significant attention. T2T communication establishes mobile ad hoc networks (MANETs), similar to those in vehicular ad hoc networks (VANETs). Building upon this foundation, we propose an adaptive cooperative (ADCO) MAC protocol for T2T communication. The scheme introduces clustering and cooperative transmission mechanisms, which enhance the efficiency and reliability of safety packet transmission. Additionally, the protocol assigns different priorities to packets engaging in contention on the control channel (CCH) and enables trains to access service channels (SCHs) without contention through pre-reserved time slots. To analyze the transmission probabilities and success rates of packets with varying priorities, a Markov-based model is utilized, ultimately determining the optimal ratio of the CCH interval (CCHI) to the SCH interval (SCHI) for maximizing channel utilization. Theoretical analysis and simulation results demonstrate that the proposed MAC protocol ensures reliable transmission of safety packets while simultaneously optimizing the throughput on SCHs.
Authors
- Pengbo Si
- Bing Bu (ORCID: https://orcid.org/0000-0001-9469-5234)
- Yong Yang (ORCID: https://orcid.org/0000-0002-4943-5625)
- F. Richard Yu (ORCID: https://orcid.org/0000-0003-1006-7594)
- Meng Li (ORCID: https://orcid.org/0000-0002-5576-9883)
- Ruizhe Yang (ORCID: https://orcid.org/0000-0003-0582-4079)
Institutions
- Beijing Jiaotong University (CN)
- Beijing University of Technology (CN)
- Carleton University (CA)
Publication Details
- Journal
- IEEE Transactions on Intelligent Transportation Systems
- Published
- 2025-09-26
- DOI
- https://doi.org/10.1109/tits.2025.3611533
- Primary Topic
- Advanced MIMO Systems Optimization
- Type
- article
- Field-Weighted Citation Impact
- 0.00