Bibliographic record
Abstract
There has been a booming interest in developing WiFi localization using multi-antenna (MIMO) access points (APs). Recent advances have demonstrated promising results that break the meter-accuracy barrier using commodity APs. Yet these state-of-the-art solutions require either multiple APs that are not necessarily available in practice, or multiple-channel measurements that disrupt normal data communication. In this paper, we present SiFi, a single AP-based indoor localization system that for the first time achieves sub-meter accuracy with a single channel only. The SiFi design is based on a key observation: with MIMO, the multiple (typically three) antennas of an AP are frequency-locked; although the accurate Time-of-Arrival (ToA) estimation on commodity APs is fundamentally limited by the imperfect time and frequency synchronization between the transmitter and receiver, there should be only one value for the ToA distortion that can cause three direct-path ToAs of the antennas to intersect at a single point, i.e., the position of the target. We develop the theoretical foundations of SiFi and demonstrate its realworld implementation with off-the-shelf WiFi cards. Our implementation introduces no hardware modification and is fully compatible with concurrent data transmission. It achieves a median accuracy of 0.93 m, which significantly outperforms the best known single AP single channel solution.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.188 | 0.167 |
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".