A Throughput Fairness-based Grouping Strategy for Dense IEEE 802.11ah Networks
Bibliographic record
Abstract
The wide range of Internet-of-Things applications has increased the number of connected devices massively. This growth may cause more contention in accessing the channel, challenging the legacy IEEE 802.11. In the IEEE 802.11ah standard, the grouping technique is exploited to make the stations (STAs) compete in a group to mitigate the contention. However, how to group the STAs in the network is still an open issue. In this paper, we propose a new strategy to group STAs in a dense network to address the above issues. We apply the MaxMin fairness criterion to the STAs' throughput to increase the overall network's performance with better fairness. Formulation of the problem results in a non-convex integer programming optimization problem which avoids hidden terminals opportunistically. As solving the optimization problem is difficult and time consuming, we apply the Ant Colony Optimization method to the problem to find the solution. Extensive simulations have been conducted to validate the solution. The proposed approach can achieve approximately up to 40% gain in the total throughput, 37% gain in the minimum per-STA throughput in the network, and 11% reduction in the number of hidden terminals compared to the existing strategies such as K-means.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".