Proportionally Fair Resource Allocation in Multirate WLAN<roman>s</roman>
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Since IEEE has a standardized 802.11 protocol for wireless local-area networks (WLANs), significant work has been done to develop rate adaptation algorithms. Most of the rate adaptation algorithms proposed till now are heuristic, suboptimal, and are competitive in nature. Even though these algorithms have the advantage of being implemented in distributed fashion, their throughput performance will be low as these schemes may converge to inefficient Nash equilibrium. On the other hand, users may cooperatively choose their rates so that a social optimum can be achieved, but there are no known algorithms that do rate adaptation cooperatively and can still be implemented in a distributed fashion. In this paper, we design a centralized algorithm that achieves a social optimum (by conducting rate adaptation cooperatively) while guaranteeing proportional fairness. We show that it converges in, at most, N iterations and has time complexity O(N2), where N is the number of users in the system. Furthermore, we propose a distributed algorithm that also serves the same purpose as the centralized algorithm.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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