Sensing Line-of-Sight Perturbations in 6-GHz Wi-Fi Using Channel Model-Based Features
Bibliographic record
Abstract
A new standard, IEEE 802.11bf, has been created to offer Wi-Fi sensing capabilities across sub-7 GHz and millimeter-wave bands. Applications of Wi-Fi sensing rely on Channel State Information (CSI) to enable various applications such as motion detection, activity recognition, and gesture recognition. Within this context, this paper investigates part of the 6-GHz spectrum for use in Wi-Fi sensing, with the aim of recognizing different types of line-of-sight (LOS) perturbation. To achieve this, a novel feature extraction methodology is presented, along with innovative features designed to comprehensively capture information from CSI. More precisely, a novel Random Forest-based algorithm is introduced that automatically selects optimal features and constructs accurate decision trees for the classification of various human interactions with the LOS link between two Wi-Fi devices. The proposed feature extraction and selection methodology leverages variations in the channel, which are manifested by the changes in the characteristics of signal propagation caused by movements in proximity of the LOS link. Using statistical channel metrics, which can be directly linked to the physical channel, enhances the efficiency and accuracy of LOS perturbation classification. A detailed set of experiments are used to demonstrate the accuracy of our approach, which we call Channel Model based Features-Random Forest (CMF-RF). CMF-RF has been shown to outperform existing methods when used to classify human interactions with the LOS link.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".