Evaluation of Tuner-Based Noise-Parameter Extraction Methods for Very Low Noise Amplifiers
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Bibliographic record
Abstract
This paper compares the performance of source-tuner noise-parameter extraction methods used to measure noise parameters of low-noise amplifiers that have very low (1 dB) noise figures. The methods discussed are known as the Cold method and the modified <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Y</i> -factor method (or Hot-Cold method). The paper describes equations used in the extraction algorithms. In a Monte Carlo analysis by randomly adding various sources of uncertainties to ¿measurements,¿ created with a computer simulation, performances of the noise parameter extraction methods are compared. It is shown that the iterative Cold method and the direct Cold method are the best at extracting <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Rn</i> and ¿ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">opt</sub> noise parameters in terms of lowest standard deviation and close proximity of the extracted mean values to the true values. The simplified Cold method, used in a number of commercial systems, has largest systematic offsets in extracted noise parameters while being the quickest to perform. The modified <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Y</i> -factor method is the slowest to perform due to additional time required for hot measurements. This method is marginally the most accurate to extract <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">min</sub> . These conclusions are also supported with measurement results. This study assembles in one place necessary theoretical background information to serve as a reference for those who are working in the field of noise parameter extraction using tuner-based methods.
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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 | 0.003 | 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