Evaluation of Tuner-Based Noise-Parameter Extraction Methods for Very Low Noise Amplifiers
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 modifiedY-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 extractingRnand ¿optnoise 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 modifiedY-factor method is the slowest to perform due to additional time required for hot measurements. This method is marginally the most accurate to extractFmin. 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".