Channel Clustering and Probabilistic Channel Visiting Techniques for WLAN Interference Mitigation in Bluetooth Devices
Why this work is in the frame
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Bibliographic record
Abstract
Since Bluetooth and wireless local area network (WLAN) technologies both operate at the 2.4-GHz industrial, scientific, and medical (ISM) band, the two types of devices may suffer from mutual interference and performance degradations. In this paper, we propose two new techniques, channel clustering and probabilistic channel visiting, to effectively improve the existing coexistence and interference mitigation mechanisms. The channel clustering technique employs statistical pattern recognition to classify the status of Bluetooth channels more accurately. The probabilistic channel visiting is used to more equitably allocate the channel resources between Bluetooth and WLAN devices. The effectiveness of these techniques is quantified by simulations. Results show that both techniques are beneficial in improving the performance of the existing mechanisms.
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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 | 0.001 | 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 | 0.000 |
| 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