Analytical Design Methodology for Generic Doherty Amplifier Architectures Using Three-Port Input/Output Networks
Bibliographic record
Abstract
In this paper, a new Doherty amplifier architecture along with an analytical based design methodology is proposed. The proposed architecture uses novel three-port network as the output matching/combining network (OMCN). The three-port OMCN performs the power combining for any arbitrary output power ratios from the two transistors of the Doherty amplifier. It also performs the impedance matching from any arbitrary complex load impedance to the optimum impedances for both transistors at peak output power. Commonly in Doherty amplifiers, an optimum performance at peak power and a sub-optimum performance at power back-off are often obtained. Using the proposed output network, optimum performance can be reached at power back-off, as well as at peak power. Another three-port network is proposed for input matching/dividing network (IMDN) at the input of the proposed Doherty amplifier. The three-port IMDN is designed to perform the power division with any arbitrary division ratio, adjust the arbitrary phase difference between the input signals to the two transistors, and provide the impedance matching from any arbitrary complex source impedance to the optimal source impedances for the two transistors. To verify the provided theory, two prototype amplifiers are designed and tested. A 12-W amplifier is designed for a 50-Ω source and load impedances at 1 GHz. Another 12-W amplifier is designed at 1 GHz for complex source and load impedances. Both amplifiers have efficiency of higher than 50% at 7-dB output power range.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.003 | 0.002 |
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".