Improving Channel Utilization in VANETs Using Q-Learning-Based Data Rate Congestion Control
Bibliographic record
Abstract
Vehicular Ad-Hoc Network(VANET) is an emerging wireless technology vital to the Intelligent Transportation System(ITS), which aims to mitigate traffic problems and improve road safety. Many VANET safety applications rely on the periodic broadcast of vehicle status information in the form of Basic Safety Messages (BSMs). When the vehicle density increases, the wireless channel faces congestion resulting in unreliable safety applications. Various decentralized congestion control algorithms have been proposed to effectively decrease channel congestion by controlling transmission parameters such as message rate, transmission power, and data rate. This paper proposes a data rate-based congestion control technique using the Q-Learning algorithm to maintain the channel load below the target threshold. The congestion problem is formulated as a Markov Decision Process (MDP) and solved using a Q-learning algorithm. The goal is to select the most appropriate data rate when transmitting a BSM such that the channel load remains at an acceptable level. Data obtained from a simulated dynamic traffic environment is used to train the Q-Learning algorithm. Our results indicate that the proposed algorithm is able to achieve the target channel load while reducing packet loss compared to existing data rate-based approaches.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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".