Moment-based SNR estimation over linearly-modulated wireless SIMO channels
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Bibliographic record
Abstract
In this paper, we develop a new method for signal-to-noise ratio (SNR) estimation when multiple antenna elements receive linearly-modulated signals in complex additive white Gaussian noise (AWGN) spatially uncorrelated between the antenna elements. We also derive extensions of other existing moment-based SNR estimators to the single-input multiple-output (SIMO) configuration. The new SIMO SNR estimation technique is non-data-aided (NDA) since it is a moment-based method and does not rely, therefore, on the a priori knowledge or detection of the transmitted symbols; it does not require the a priori knowledge of the modulation type or order. The new method is shown by Monte Carlo simulations to clearly outperform the best NDA moment-based SNR estimation methods in terms of normalized root mean square error (NRMSE) over QAM-modulated transmissions, namely the M <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> M <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> method and the estimators referred to, in this paper, as the GT and the M <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sub> methods, even when we extend them to the SIMO configuration.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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