BLE phase-based ranging: accuracy and capability under strong Wi-Fi interference
Bibliographic record
Abstract
Indoor positioning and asset tracking have become popular and essential for different applications and use cases. Many systems use Bluetooth Low Energy (BLE) wireless personal area network technology for communication and ranging purposes. Unfortunately, due to limitations of the ISM radio band, other communication technologies such as Z-Wave, ZigBee, and Wi-Fi also use the same frequency bandwidth. This overlap often leads to interference that affects the performance of BLE systems. This work evaluates the effect of Wi-Fi interference on the phasebased ranging distance estimate for different BLE to Wi-Fi signal power ratios. We show the random distance error increasing more than 3 times for both Inverse Fourier Transform and Multiple Signal Classification algorithms at short distances. Based on simulation results and infield experiments, we identified that the interference becomes marginal for distances more than 10m, and the device can’t identify the location correctly in case of similar Wi- Fi and BLE Tx power. In the case of long-distance ranging, ignoring interfered frequencies improves the situation dramatically, but this results in worse resolution and sometimes may identify the distance incorrectly due to false peaks.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".