Power Efficiency and Linearity Enhancement Using Optimized Asymmetrical Doherty Power Amplifiers
Bibliographic record
Abstract
This paper investigates the virtues of the asymmetrical Doherty power amplifier (PA) for improving the average power efficiency, linearity, and peak envelope power. It commences with an in-depth study of the effects of increasing the size of the peaking amplifier's transistor and its conduction angle on the Doherty PA's RF performance. In particular, the impact of the extended current profile of the peaking amplifier and reduced turn-on effects on the soft-turn characteristic are thoroughly analyzed, and their impacts on the average efficiency and peak power are deduced. Furthermore, the aggravation of the memory effects that accompany the gm3-based nonlinear distortion cancellation is experimentally demonstrated. Two asymmetrical Doherty PAs prototypes are fabricated using 80 W and 150 W laterally diffused metal oxide semiconductor field-effect transistors to individually improve average efficiency and linearity. When driven with a four carrier wideband code division multiple access (4C-WCDMA) signal, the asymmetrical Doherty PA allowed for excellent drain efficiency of approximately 50%, along with high linearity of approximately -50 dBc , using a memory polynomial digital predistorter at an average output power of 50 W. To the best of the authors' knowledge, this achieved efficiency is the highest reported in the literature for a high-power Doherty PA implemented in LDMOS technology.
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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 | 0.000 | 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) | 0.001 | 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 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".