A Token-Based Scheduling Scheme for WLANs and Its Performance Analysis
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Bibliographic record
Abstract
Most of the existing WLAN MAC protocols can only provide limited service differentiation. In this paper, we propose a novel token-based scheduling scheme for precise and quantitative service differentiation, which can provide great flexibility and facility to the network service provider for service class management. Simulation results demonstrate that the proposed scheme can effectively achieve proportional differentiation among different classes, while achieving fair resource sharing within the same class. In addition, compared with the contention based scheme and the centralized polling scheme, the proposed scheme significantly improves the channel utilization by avoiding collisions (with the contention based scheme) and the polling overhead (with the polling scheme). The performance analysis of the proposed scheme is also presented. The accuracy of the analytical results are verified by computer simulations.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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