Toward a More Generalized Doherty Power Amplifier Design for Broadband Operation
Bibliographic record
Abstract
The conventional Doherty power amplifier (DPA) theory is limited to single carrier operations, leading to a nongeneric structure. This paper presents a new analysis that generalizes the conventional DPA (CDPA) theory for increased efficiency and bandwidth. We demonstrate that by introducing a theoretical parameter α at the output combiner, we can redefine the relationships among the output combiner elements for a greater level of design flexibility than it was possible in the CDPA. We also show that previously published works in this area can be considered as special cases of the proposed general theory. As a demonstrator, a specific design, named reduced-α DPA, realized using GaN HEMTs is provided to illustrate the robustness of the approach. This design proves effective for further improving the performance of the previously published 2.14-/2.655-GHz dual-band parallel DPA. A maximum drain efficiency of 84% and 67% at an average of 43-dBm peak and 6-dB back-off power levels, respectively, was measured with continuous wave signals. To quantify the linearity performance, the proposed DPA was tested using wideband CDMA and long-term evolution signals where the adjacent channel leakage ratio was recorded at -25 dBc with an average output power of 38.7 and 36.5 dBm at 2.14 and 2.655 GHz, respectively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 | 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".