Generic load-pull-based design methodology for performance optimisation of Doherty amplifiers
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Bibliographic record
Abstract
In this study, a systematic design methodology is proposed to optimise the operation of Doherty power amplifiers (PAs). The proposed approach makes use of two sets of load-pull data to enhance the performance of Doherty PAs at low- and high-power-drive levels. The first load-pull, which is performed on the device operating at saturation, permits one to maximise the performance at a high-power region. The second load-pull, which is performed at the power level associated with the turn-on of the peaking amplifier, aims to boost the performance at back-off. To assess its effectiveness, the proposed methodology is applied to design three Doherty PAs sought for power efficiency, linearity and gain, respectively. Around the Doherty turn-on point, these circuits achieved up to 9% efficiency improvement, up to 10 dB inter-modulation reduction and up to 2 dB gain improvement, respectively. For experimental validation, a gallium-nitride (GaN)-based Doherty PA prototype sought for efficiency was implemented. The fabricated Doherty PA demonstrated a power-added efficiency (PAE) higher than 40% over an output power back-off (OPBO) range of 8 dB, with two peak PAE points of 52 and 62% located at 6.8 dB OPBO and at saturation, respectively.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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