Decimeter Ranging With Channel State Information
Why this work is in the frame
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Bibliographic record
Abstract
This paper aims at the problem of time-of-flight (ToF) estimation using channel state information (CSI) obtainable from commercialized multiple-input-multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) wireless local area network (WLAN) receivers. It was often claimed that the CSI phase is contaminated with errors of known and unknown natures rendering the ToF-based positioning difficulty. To search for an answer, we take a bottom-up approach by first understanding CSI, its constituent building blocks, and the sources of error that contaminate it. We then model these effects mathematically. The correctness of these models is corroborated based on the CSI collected in extensive measurement campaign, including radiated, conducted, and chamber tests. Knowing the nature of contaminations in the CSI phase and amplitude, we proceed with introducing pre-processing methods to clean CSI from those errors and make it usable for range estimation. To check the validity of the proposed algorithms, the MUSIC super-resolution algorithm is applied to post-processed CSI to perform range estimates. The results substantiate that a median accuracy of 0.7, 0.8, and 0.9 m is achievable in a highly multipath line-of-sight environment where the transmitter and the receiver are 5, 10, and 15 m apart.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it