A Broadband Doherty Power Amplifier Based on Continuous-Mode Technology
Bibliographic record
Abstract
In this paper, a novel broadband Doherty power amplifier based on the continuous-mode technique (C-DPA) is proposed. The amplifier is focused on manipulating harmonic components in a Doherty power amplifier (DPA) structure to achieve improved bandwidth and efficiency. In a conventional DPA, harmonic isolation is typically required between the two transistors to prevent them from modulating each other at harmonic frequencies. However, as presented in this paper, such isolation is not actually necessary. On the contrary, by allowing the two transistors to modulate each other at harmonic frequencies with the help of a properly designed postharmonic tuning network, a series of highly efficient DPA modes can be created over a continuous frequency band, leading to a broadband C-DPA. Based on the proposed method, an example of a C-DPA working from 1.65 to 2.75 GHz was designed. According to the measured results, the designed C-DPA exhibits a 52%-66% efficiency at a -6 dB power backoff and a power utilization factor higher than 1.08 over the 1.1-GHz band. In addition, when simulated by a 7.5-dB peak-to-average power ratio 20-MHz LTE signal, the example C-DPA exhibits an efficiency of 46%-62% while maintaining an adjacent channel power ratio below -45 dBc after linearization over the full 1.1-GHz band. To the best of our knowledge, this is the first proposed C-DPA and a state-of-the-art performance for broadband DPAs.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".