A Time-Domain Multi-Tone Distortion Model for Effective Design of High Power Amplifiers
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Bibliographic record
Abstract
This paper proposes a new time-domain multi-tone distortion (TD-MTD) model suitable for accurately predicting the non-linear behavior of packaged high power radio frequency (RF) transistors over a range of discrete non-uniformly distributed frequencies. This proposed TD-MTD model uses a single expression rather than multiple distinct frequency specific behavioral models to describe the underlying behavior of the high power RF transistor at multiple fundamental frequencies. Furthermore its extraction is carried out using a time-domain representation of the travelling waves that can be acquired using a generic vector load-pull characterization system and without imposing additional requirements. The proposed model is extracted as an artificial neural network (ANN) and is implemented as a Netlist to serve in a harmonic balance simulator based power amplifier design process. The proposed model is validated in two phases. First, its ability to reproduce the large-signal behavior of a high power RF LDMOS transistor was demonstrated in simulation. Then, the TD-MTD model was used to validate the design of a high power two-way asymmetric Doherty power amplifier and the simulated output-power-dependent power efficiency, AM/AM, AM/PM and input return loss characteristics were compared to those obtained in measurement. The excellent agreement between the simulation and measurement results confirms the usefulness of the proposed model despite the simplicity of its extraction routine and measurement data.
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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 | 0.000 |
| 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