Quantifying Quality of Service differentiation for WiMAX networks
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
Quality of Service (QoS) differentiation in WiMAX networks is a critically important part of QoS support that has not been properly addressed in the literature. Radio Resource Management (RRM) techniques such as packet scheduling and admission control have been studied by many research groups, and although they claim to provide QoS differentiation in their schemes, no studies have truly evaluated and measured the QoS differentiations among various service classes. In this study, we propose quantifying QoS differentiation using a new parameter called Fairness, which could measure QoS differentiation among various service classes. By simulation results, we show that Fairness values could provide detailed information about QoS differentiation, where throughput and delay fall short of comparison analysis for QoS support among Real Time (RT) versus Non-RT (NRT) applications.
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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 | 0.000 |
| 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