Bandwidth-Expanded High-Linearity Doherty Power Amplifier With Improved AM–AM and AM–PM Characteristics
Bibliographic record
Abstract
This article proposes a novel design methodology for a high-efficiency, wideband linear Doherty power amplifier (DPA). A collaborative approach is employed in the design, which introduces phase mismatch between the two branches and incorporates a novel load-matching network (LMN) with additional compensation branches. Through theoretical analysis and derivation, it is shown that this method not only produces predefined nonlinear amplitude-to-amplitude (AM–AM) and amplitude-to-phase (AM–PM) characteristics through LMN but also significantly expands the broadband high-efficiency design space of the DPA. To validate the proposed theory, a linear high-efficiency gallium nitride (GaN) DPA operating between 1.85 and 2.3 GHz with a relative bandwidth of 21.7% is designed and fabricated. The measurement results indicate that the fabricated DPA achieves a saturated output power ranging from 43.2–43.7 dBm, with a saturated drain efficiency (DE) of 60%–68% and a 6-dB back-off DE of 50%–55%. Furthermore, ignoring the performance degradation at the edge frequency of 2.3 GHz, the fabricated DPA exhibits an AM–AM variation of less than 1 dB and an AM–PM variation of less than 3.5° over the 1.85–2.25 GHz range. At an average output power of approximately 35 dBm, the fabricated DPA exhibits an EVM of less than 2.8% and an adjacent channel power ratio (ACRR) lower than −38.1 dBc across the entire operating frequency band without digital predistortion (DPD) when driven by a 60-MHz 5G new radio (NR) signal with a peak-to-average power ratio (PAPR) of 8.1 dB in the TM3.1 test mode. Similarly, under a 100-MHz 5G NR signal at 8.1-dB PAPR in the TM3.1 test mode, the original EVM without DPD in the operating frequency band is less than 3.6% and ACRR is lower than −35.1 dBc.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".