Three-Dimensional Markov Chain Model for Performance Analysis of the IEEE 802.11 Distributed Coordination Function
Bibliographic record
Abstract
This paper introduces an accurate analysis using three dimensional Markov chain modeling to compute the IEEE 802.11 DCF performance under heavy traffic conditions and absence of hidden terminals. The proposed model matches the real implementation of the DCF as presented in the standard through considering the impact of retry limits of control and data frames jointly on the performance of DCF mechanism. Moreover, transmission errors are added to the model as constant frame error probabilities. In addition to the throughout efficiency, this analytical analysis calculates the average packet delay, the packet drop probability and the average packet drop time for the DCF access modes, basic and RTS/CTS. We show that our proposed model is a more general model compared to the other models that are presented in the literature, which leads to more accurate performance analysis. Simulation results validate the accuracy of our analytical analysis. Moreover, we prove the generality and validate the correctness of our analysis by showing that other models appeared in literature are special cases from our proposed model. Moreover, the impact of the retry limits and the network size on the performance of IEEE 802.11 DCF is presented.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".