Machine Learning for Location and Orientation Fingerprinting in MIMO WLANs
Bibliographic record
Abstract
To detect the position and the orientation of a mobile device within a Wireless Local Area Network (WLAN) covered by multiple access points (APs), the intrinsic properties of multiple-input multiple-output (MIMO) channels are used linking the received signal strength indicators (RSSIs) to the distance and exploiting the received signal correlation structures. Location and orientation fingerprinting is a map based positioning solution that stores for a given orientation past measurements of RSSIs at known reference/grid points in a database that is later used to localize a mobile device at an unknown location and with unknown orientation to the closest reference point. This paper focuses on processing the RSSI data vectors from multiple receiving antennas on a downlink by applying the core tools of Machine Learning (ML) classification methods to evaluate the effects of MIMO RSSI meta-data when capturing 802.11n/ac packets using commodity hardware. Specifically, the paper provides insights into the design of the overall location fingerprinting system operating with new WiFi physical link layer protocols. To verify the operation of the proposed system, experimental results are presented to investigate the impact of different factors, like the number of receive antennas, affecting the estimation accuracy for the location and the orientation of mobile user.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".