Linearity-Enhanced Doherty Power Amplifier Using Output Combining Network With Predefined AM–PM Characteristics
Bibliographic record
Abstract
In this paper, a new method is proposed to synthesize a linearity-enhanced Doherty power amplifier (DPA) without deteriorating its efficiency. This method determines the combiner network parameters so that a predefined amplitude-to-phase (AM-PM) characteristic is produced while maintaining proper load modulation and consequently good back-off efficiency. The predefined AM-PM characteristic is chosen to be the inverse of the main transistor to enhance the overall DPA linearity. For proof-of-concept validation purposes, a linearity-enhanced DPA circuit prototype is designed to provide linear overall AM-PM characteristics over the frequency band of 4.7-5.3 GHz. Meanwhile, its input matching network is designed to minimize the amplitude-to-amplitude (AM-AM) distortion by properly selecting the source impedances. The measurement results of the DPA prototype under continuous-wave stimuli reveal AM-PM and AM-AM characteristics with maximum phase and gain compression/expansion below ±1° and ±0.25 dB, respectively, when the input power level is swept up to a saturation level of 39 dBm over 4.9-5.3 GHz. Furthermore, when driven with carrier aggregated signals with modulation bandwidths of up to 160 MHz and a peak-to-average power ratio equal to 7.4 dB, the DPA prototype maintains an adjacent channel leakage ratio of better than -40 dBc with a drain efficiency in the excess of 40% and an average output power of 32 dBm, without resorting to any additional linearization schemes. The proposed DPA methodology paves the road for the application of the DPA technique to 5G massive multiple-input and multiple-output transmitters with relaxed linearity requirements as it avoids the extra complexity and power consumption overhead associated with dedicated linearization schemes.
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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 | 0.000 |
| 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 | 0.000 |
| 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".