Efficient Wi-Fi signal strength maps using sparse Gaussian process models
Why this work is in the frame
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Bibliographic record
Abstract
This objective of this paper is to propose and evaluate a new algorithm to increase the computation and storage efficiency and to reduce the bandwidth requirements of the Wi-Fi received signal strength indicator (RSSI) maps based on Gaussian Process (GP) models. GP models are non-parametric models that estimate the likelihood function of the target variable, in this case the Wi-Fi RSSI values, conditioned on a set of training data. This paper introduces the Parametric Grid Sparse GP (PGSGP) algorithm, to improve the efficiency of using GP maps. The PGSGP reduces the complexity of evaluating the likelihood function, by reducing the number of points in the training dataset, without significant loss of the mapping or positioning accuracy. This is achieved by finding a set of pseudo-inputs arranged over a parametric grid, then optimizing the corresponding target values and the GP model hyperparameters.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| 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