Resource Reservation Coordination for Vehicle Platooning in C-V2X Networks
Bibliographic record
Abstract
High-reliability and low-latency communication is essential for timely information exchange in vehicle platooning. As a key enabler of this, the cellular vehicular-to-everything (C-V2X) network uses a sensing-based semi-persistent scheduling (SPS) protocol, where radio resources are reserved for a number of transmissions with reduced resource re-allocation and control overhead. However, consecutive access collisions may be caused by reservation conflict, which leads to long delay and threatens platoon’s stability and safety. In this paper, a coordinating resource reservation (CRR) protocol is proposed for vehicle platooning. By implementing error detection with coordination among platoon vehicles, the resource reservation is improved for reduced collisions and delay. Specifically, packet reception/loss information is sent out by platoon vehicles through their own packets. Such information is shared with transmitters and guides them to reserve new resources when access collision occurs. As a result, long delay is avoided while no extra feedback packet is introduced. Furthermore, Markov analysis is presented to evaluate the performance of SPS and the proposed CRR for vehicle platooning, providing the quantified performance gains. Finally, simulation results demonstrate the superiority of the proposed CRR in reducing packet loss and latency, compared with the legacy SPS and other state-of-the-art solutions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".