Modeling and performance evaluation of frame bursting in wireless LANs
Bibliographic record
Abstract
Several enhancements to the Medium Access Control (MAC) layer of the IEEE 802.11 standard have been recommended in the new 802.11e standard. One main enhancement is the possibility to send bursts of frames during a limited duration called Transmission Opportunity (TXOP). Similar MAC efficiency enhancements such as Frame Aggregation have also been proposed for the upcoming 802.11n standard. We analyze the application of this feature to the existing 802.11 MAC and evaluate its performance under different network conditions. The frame bursting feature can increase the total capacity of an 802.11 network by reducing the contention and collision for bursty traffic sources. We extend the established 802.11 analytical models to include the frame bursting feature. Through simulation experiments we analyze the delay performance of the network under different frame bursting options and show that while in general frame bursting is useful and can increase the system capacity, it might cause excessive unfairness in certain cases. Using the findings of this article we present guidelines for implementing fair adaptive algorithms that use TXOP.
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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) | 0.000 | 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 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".