Performance analysis of grouping strategy for dense IEEE 802.11 networks
Why this work is in the frame
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Bibliographic record
Abstract
In IEEE 802.11 networks, how to improve the efficiency of contention-based media access is an important, challenging issue. Recently, the grouping strategy is introduced in the IEEE 802.11ah standard to alleviate the channel contention. In IEEE 802.11ah networks, stations can be divided into groups and each group is only allowed to access wireless channel during the designated channel access period. By limiting the number of stations participating in the channel contention, it is anticipated that such a grouping strategy could substantially improve the communication efficiency. However, how to allocate the channel among different groups and how to adjust the number and sizes of groups are still open issues. In this paper, we first study the impact of the grouping strategy on the network performance, and then propose an analytical model to track the performance under saturated traffic. The accuracy of our model has been validated by simulation results. Our analytical model and results also provide important guidelines in optimizing grouping parameters.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 it