Fair scheduling with bottleneck consideration in wireless ad-hoc networks
Why this work is in the frame
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Bibliographic record
Abstract
Most research work in the area of wireless ad-hoc networks attempts to balance the trade-off between fairness and channel utilization. In this paper, we first propose a topology-independent methodology to predict maximum achievable channel utilization under fairness constraint by two performance bounds. Based on the notion of bottlenecks introduced in prediction, we design a centralized and improved fair scheduling algorithm for wireless ad-hoc networks. We capture traffic load characteristics by using a proposed parameter that represents the "contending power" of nodes in the weighted flow contention graph. Finally, we demonstrate the effectiveness of our proposed algorithm through both provable analysis and simulations, and discuss natural derivations of a fully distributed algorithm using our bottleneck-based analytic model.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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