GPS-Assisted Path Loss Exponent Estimation for Positioning in IEEE 802.11 Networks
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Bibliographic record
Abstract
We present a new adaptive method to calculate the path loss exponent (PLE) for microcell outdoor dynamic environments in the 2.4 GHz Industrial, Scientific, and Medical (ISM) frequency band. The proposed method calculates the PLE during random walks by recording signal strength measurements from Radio Frequency (RF) transceivers and position data with a consumer-grade GPS receiver. The novelty of this work lies in the formulation of signal propagation conditions as a parametric observation model in order to estimate first the PLE and then the distance from the received RF signals using nonlinear least squares. GPS data is used to identify long term fading from the received signal's power and helps to refine the power-distance model. Ray tracing geometries for urban canyon (direct line of sight) and nonurban canyon (obstacles) propagation scenarios are used as the physics of the model (design matrix). Although the method was implemented for a lightweight localization algorithm for the 802.11b/g (Wi-Fi) standard, it can also be applied to other ISM band protocols such as 802.15.4 (Zigbee) and 802.15.1 (Bluetooth).
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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 | 0.000 |
| 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 it