Cross-layer interference minimization-oriented channel assignment in IEEE 802.11 WLANs
Bibliographic record
Abstract
IEEE 802.11 wireless local area networks (WLANs) are widely deployed nowadays in home and urban areas. To solve the problem of radio frequency scarcity, interference minimization-oriented channel assignment has been very much explored at PHY layer. However, the effect of time domain simultaneous transmission on the neighboring interference is rarely considered. In this paper, we propose a cross-layer channel assignment algorithm for newly deployed access point (AP) initial setting up in high-density WLANs. Compared to the conventional algorithms which only focus on PHY layer, our proposed algorithm jointly analyzes the time domain overlap from MAC layer and frequency domain overlap at PHY layer to minimize the neighboring interference, which is the fatal reason of network Quality of Service (QoS) reduction. We also modify the beacon frame to support real-time information collection for channel assignment. Simulation results are provided to validate the proposed cross-layer channel assignment algorithm.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".