Scheduling alternatives for mobile WiMAX end-to-end simulations and analysis
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".