Selectivity function scheduler for IEEE 802.11e HCCA access mode
Bibliographic record
Abstract
SUMMARY In this paper, we present a scheduling algorithm that enhances the performance of the standard IEEE 802.11e scheduler for the Hybrid Coordination Function Controlled Channel Access mode. The main contribution in designing the proposed scheduler is the ability to accommodate multiple streams with different levels of Quality of Service requirements concurrently running on the same station. This is achieved by dynamically calculating the Transmission Opportunities of each active traffic stream (TS) and the appropriate Service Interval of each active station. The proposed algorithm optimizes the utilization of the scarce bandwidth resources by only polling active stations. The algorithm incorporates a selectivity function to assign polling priorities to the active streams only based on their diverse requirements and their link‐attainable transmission rates. The performance of the proposed Selectivity Function Scheduler (SFS) scheme is evaluated against the standard scheduler. Simulation results show that the SFS outperforms the standard scheduler in terms of enhancing streams' throughput, reducing packet dropping ratio and maintaining high fairness amongst the admitted TS. Copyright © 2011 John Wiley & Sons, Ltd.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".