Addressing the Start-of-Interval Contention Issue in WAVE Protocol Using Reinforcement Learning
Bibliographic record
Abstract
Intelligent Transportation System (ITS) applications rely on reliable data exchange, supported by the WAVE protocol through vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. In single-radio configurations, nodes alternate between a control channel (CCH) and service channels (SCHs), leading to a burst of queued transmissions at the start of each SCH interval. This results in the start-of-interval contention (SIC) issue, characterized by high collision rates and reduced delivery performance. Therefore, we propose a centralized contention window (CW) adaptation mechanism based on the Quantile Regression Deep Q-Network (QR-DQN), where a Roadside Unit (RSU)-hosted agent adjusts CW using only PHY-layer observations. We further combine this deep reinforcement learning (DRL)-based method with the Skip-CCH mechanism and evaluate both individual and combined solutions through simulation. Results show notable improvements in packet delivery and channel efficiency compared to legacy WAVE and Skip-CCH.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".