Comparative Analysis of Single-Box and Two-Box RF Power Amplifiers’ Behavioral Models Sensitivity to Delay Misliagnment
Bibliographic record
Abstract
This paper examines the sensitivity of power amplifiers behavioral models to time-delay misalignment. Memory polynomial and twin-nonlinear two-box models are derived, from measured data, to model the behavior of a GaN based Doherty power amplifier under various time misalignment conditions. The results show that the memory polynomial model is much less sensitive to delay misalignment than twin-nonlinear two-box models. In particular, the memory polynomial model accuracy is not altered by a delay underestimation up to one sample, and is only degraded when the delay is overestimated. Conversely, twin-nonlinear two-box models performances are degraded whether the delay is underestimated or overestimated. Hence, the identification of twin-nonlinear two-box models unavoidably requires accurate delay alignment with a sub-sample resolution while the memory polynomial model can be derived from low complexity coarse delay alignment. Thus, in comparison with the two-box models, the complexity of the memory polynomial model identification can be offset by the use of low-complexity coarse time-delay alignment algorithms.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".