AP Association Optimization and CCA Threshold Adjustment in Dense WLANs
Bibliographic record
Abstract
Dense deployment of wireless local area networks (WLANs) is part of the next generation Wi-Fi and standardization (802.11ax) efforts are underway. However, dense deployment of WLAN access points (APs) faces increased interference and uncoordinated association of user stations (STAs) with APs, which degrade network throughput. To assess the potential of improving uplink throughput in the presence of interference using AP association coordination, we propose an association optimization algorithm that matches STAs to APs in dense WLAN (DWLAN). While existing cell breathing approaches suggest tuning of APs' beacon powers for association control, the proposed approach utilizes uplink signal- interference-noise ratio (SINR) of stations (STAs) to coordinate STA-AP association. In order to further coordinate interference and increase spatial reuse, an algorithm is proposed to adjust the clear channel assessment (CCA) threshold of the 802.11 MAC protocol in each AP cell to address the problem of overlapped basic service set (OBSS) that degrades overall network throughput. Performance evaluation reveals that our SINR-based AP association coordination and CCA threshold adjustment schemes achieve significant increase in per-user throughput in a DWLAN.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".