A Methodology and a Metric for the Assessment of the Linearizability of Broadband Nonlinear Doherty Power Amplifiers
Bibliographic record
Abstract
This letter proposes a novel methodology and a metric for the assessment of the linearizability of broadband nonlinear power amplifiers (PAs). Validation of the proposed methodology and the proposed metric on two linearized Doherty PAs (DPAs) using digital predistortion (DPD) technique was carried out and it demonstrated the suitability and appropriateness of the new metric. Different from the iterative optimization procedure between the PA circuit design and the DPD algorithm compensation, the proposed method aims to provide a quantitative criterion on the PA linearizability based on its frequency-dependent amplitude modulation (AM)/AM and AM/phase modulation (PM) characteristics. To verify the effectiveness of the metric, two DPAs were characterized before and after a DPD-based linearization. The average correlation coefficients between the values of the metric and the adjacent channel power ratio after DPD were 0.8763 and 0.9156, and that between the values of the metric and the error vector magnitude after DPD were 0.8618 and 0.7200 for PA1 and PA2, respectively, indicating that the evaluation method and the linearizability metric can be used as a good measure to assess the linearizability of broadband PAs.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".