RSS Localization Under Gaussian Distributed Path Loss Exponent Model
Bibliographic record
Abstract
We consider localization from the received signal strength (RSS) when the transmit power and the log-distance pathloss exponent (PLE) are unknown. The unknown transmit power problem is handled by working with the difference of RSS (DRSS) from a reference node. The unknown PLE is statistically modelled as a Gaussian distributed random variable. A maximum-likelihood estimation procedure is firstly proposed to obtain the ratio-of-distances in closed-form. Next, in order to obtain the source location from the ratio-of-distance estimates, we propose a two-step linear least squares (TLLS) estimator which exploits the known relation between the source coordinates and the range variable. Finally, we propose a maximum-a-posteriori (MAP) estimator which jointly estimates the source location and the PLE by maximizing the posterior likelihood of the DRSS values, given the distribution of the PLE. Numerical studies validate the improved localization accuracy of the proposed estimators over the state-of-the-art.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".