Design and Optimization of Adaptive Cooperative MAC Protocol With Priority Scheduling for Train-to-Train Communications
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".