Decentralized AP Selection in Large-Scale Wireless LANs Considering\n Multi-AP Interference
Bibliographic record
Abstract
Densification of access points (APs) in wireless local area networks (WLANs)\nincreases the interference and the contention domains of each AP due to\nmultiple overlapped basic service sets (BSSs). Consequently, high interference\nfrom multiple co-channel BSS at the target AP impairs system performance. To\nimprove system performance in the presence of multi-BSSs interference, we\npropose a decentralized AP selection scheme that takes interference at the\ncandidate APs into account and selects AP that offers best\nsignal-interference-plus noise ratio (SINR). In the proposed algorithm, the AP\nselection process is distributed at the user stations (STAs) and is based on\nthe estimated SINR in the downlink. Estimating SINR in the downlink helps\ncapture the effect of interference from neighboring BSSs or APs. Based on a\nsimulated large-scale 802.11 network, the proposed scheme outperforms the\nstrongest signal first (SSF) AP selection scheme used in current 802.11\nstandards as well as the mean probe delay (MPD) AP selection algorithm in [3];\nit achieves 99% and 43% gains in aggregate throughput over SSF and MPD,\nrespectively. While increasing STA densification, the proposed scheme is shown\nto increase aggregate network performance.\n
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| 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 teacher head, 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".