A controlled-access scheduling mechanism for QoS provisioning in IEEE 802.11e wireless LANs
Bibliographic record
Abstract
Wireless Local Area Networks (WLAN) are being deployed at a rapid pace and in different environments. As a result, the demand for supporting a diverse range of applications over wireless access networks is becoming increasingly important. In particular, multimedia applications, such as Video and Voice, have specific delay and bandwidth requirements that cannot be fulfilled by the current IEEE 802.11-based WLANs. To overcome this issue, new enhancements are being introduced to the Medium Access Control (MAC) layer of the 802.11 standard under the framework of the IEEE 802.11e standard which is still a work in progress. The 802.11e standard offers new features for supporting Quality of Service (QoS) in the MAC layer, it however does not mandate a final solution for QoS issues and intentionally leaves it to the implementers to devise their own methods using the available features. We present a solution that employs the controlled access features of the 802.11e to provide per-session guaranteed quality-of-service. Our design comprises of a scheduler that assign guaranteed service times to individual sessions using a fair scheduling algorithm. We show that the proposed solution outperforms other methods that are contention and priority based.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".