Digital Doherty Amplifier With Enhanced Efficiency and Extended Range
Bibliographic record
Abstract
In this paper, a digital Doherty power amplifier (PA) with improved efficiency performance over a wide power range is proposed. The efficiency enhancement is achieved by using a digitally controlled dynamic input power distribution scheme implemented to improve the active load modulation mechanism and to minimize the drive power waste into the peaking branch at backed-off power levels. Furthermore, the proposed distribution scheme causes the premature saturation of the carrier amplifier of the proposed Doherty PA and results in an extended range of high back-off efficiency. A comprehensive study of the operational principle of the proposed efficiency-extended digital Doherty PA is provided to demonstrate its merits and to enlighten its operation. In particular, the current and power profiles of the proposed digital Doherty PA are exposed and its efficiency characteristics analyzed. For experimental validation, the proposed Doherty PA is implemented within the dual-input digitally driven architecture based on a 10-W gallium-nitride transistor. Using a one-carrier Worldwide Interoperability for Microwave Access signal with a 9-dB peak-to-average power ratio and 10-MHz bandwidth, the digitally linearized efficiency-extended Doherty PA exhibited an excellent drain efficiency of 50% along with - 38 dB of relative constellation error. The efficiency enhancement is 7% in comparison to a conventional fully analog Doherty PA.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".