Generic load-pull-based design methodology for performance optimisation of Doherty amplifiers
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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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".