Performance analysis of controlled access phase scheduling for per-session QoS provisioning in IEEE 802.11e WLANs
Bibliographic record
Abstract
The widespread deployment of IEEE 802.11 based wireless local area networks (WLAN) has made broadband access a reality for many consumers. As a result, supporting a wide range of applications, in particular networked multimedia applications, has become of increasing importance. Since specific delay and bandwidth requirements of multimedia applications cannot be fulfilled by the current IEEE 802.11-based WLANs, 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. Nevertheless, the 802.11e only provides the means of supporting quality of service (QoS) in the MAC layer and does not mandate a final solution for QoS issues. We present a QoS solution that employs the controlled access features of the 802.11e to provide per-session guaranteed QoS. Our design comprises of a scheduler that assigns guaranteed service times to individual sessions using a fair scheduling algorithm. Through analysis and experiments we prove the fairness of the algorithm and show that the proposed solution outperforms other methods that are contention or priority based
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".