Estimation of Ricean and Nakagami distribution parameters using noisy samples
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Bibliographic record
Abstract
The problem of estimating the Ricean and Nakagami-m distribution parameters in noisy slowly fading channels is studied. Previous published works have mainly examined estimation based on a noiseless sample model. The predicted performances of these estimators can only be achieved by having knowledge of the values of the individual noise samples and subtracting them from the noisy signals, an impractical case. In this paper, a system model which uses samples corrupted by noise is examined. The probability density functions of noisy channel samples are derived. Novel maximum likelihood estimators as well as moment-based estimators for operation in noisy environments are developed based on these density functions. The sample means and sample root mean square errors of the estimators are determined. Numerical results show the new estimators have superior performances over estimators designed for noiseless samples in applications where noise is present.
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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