Scheduling alternatives for mobile WiMAX end-to-end simulations and analysis
Why this work is in the frame
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Bibliographic record
Abstract
Fourth Generation broadband wireless technologies such as WiMAX and LTE depend heavily in the performance of their schedulers to deliver high data throughput and meet quality-of-service commitments. This paper compares four different proposed schedulers for mobile WiMAX (Proportional Fairness (PF), Multiclass Modified Largest Weighted Delay First (MLWDF), Highest Urgency First (HUF), and Weighted Fair Queuing (WFQ) )in a range of environments. The evaluation is based on five industry-defined key performance indicators: average sector throughput, application throughput, average completion time, fairness index and delay). The schedulers are evaluated under three simulated environments: controlled (with a detailed analysis of each algorithm's behavior in terms of throughput over time), stationary and mobile. The controlled environment provides interesting insights about the behavior of flows with identical QoS parameters and different RF conditions, and helps to validate subsequent results obtained in the other two environments. Our results for the stationary and mobile environments show that all algorithms meet quality-of-service requirements within system capacity. Algorithms that maximize spectral efficiency (PF and MLWDF) also achieved considerable throughput improvements. MLWDF's throughput results, while outperforming all other schedulers under stationary conditions, fall behind PF in the mobile scenario. The variability introduced by the mobile environment yields no statistically significant difference among the schedulers.
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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