Achieving Efficiency and Fairness in 802.11-Based Vehicle-to-Infrastructure Communications
Why this work is in the frame
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Bibliographic record
Abstract
An efficient medium access control (MAC) protocol should yield maximum throughput and fairness. However, because these two performance metrics have conflicting interests, an effective solution must address the trade-off between them. In this paper, we study the performance of the IEEE 802.11p MAC protocol in vehicle-to-infrastructure communications. The main focus is the trade-off between the system throughput and the level of fairness among the communicating nodes, which leads to a customized optimization problem. Accordingly, we formulate a multiobjective optimization problem to optimize both throughput and fairness, and propose a dynamic mechanism to maintain fairness among the mobile nodes. The optimization problem is solved by means of an approximate numerical solution, the results of which demonstrate the effectiveness of the proposed MAC scheme in terms of throughput and short-/long-term fairness.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it