Saturation Throughput Analysis of IEEE 802.11e Enhanced Distributed Coordination Function
Bibliographic record
Abstract
The IEEE 802.11 Task Group E will soon approve the 802.11e standard for medium access control (MAC) layer quality-of-service (QoS) enhancements to the 802.11 protocol, and it is widely believed that these enhancements will allow 802.11 technology to form the foundation of high-bandwidth vertically integrated networks. At the heart of 802.11e is a modified contention-based access mechanism, named the enhanced distributed coordination function (EDCF). In this paper, we propose and validate an analytical model for the saturation throughput of EDCF. Key to the accuracy of our model is a treatment of the postcollision period, which has been ignored by all previous 802.11 models. With results from the ns-2 simulator, we show that our model can accurately predict throughput over a wide range of scenarios, and thereby demonstrate its usefulness as a predictive tool for use in QoS provision. With context provided by our analytical model, we discuss the primary throughput differentiation mechanisms of EDCF.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".