Analytical Modeling of the Dual-Input Digital Doherty Power Amplifier for Efficiency and Linearity Optimization
Bibliographic record
Abstract
The paper presents a comprehensive theoretical analysis of dual-input digital Doherty power amplifiers (DPAs) and outlines a specific design methodology. The theory explains how adaptive input signals affect the linearity and efficiency of the amplifiers, as well as the necessary adjustments to the operating point of the peaking amplifier. Using the analytical model, the paper offers guidance on adjusting the input signal to achieve the highest efficiency, ensuring that the carrier amplifier remains in saturation mode within the load-modulated region. It also discusses the maximum theoretically achievable efficiency for digital DPAs, which serves as a benchmark for comparing results with other amplifier topologies. Additionally, the paper proposes a practical approach for implementing the adaptive input algorithm during the measurement of modulated test signals. This method combines the measured response of the DPA with the theoretically derived optimal input signal splitting pattern, creating test signals for both the carrier and peaking input paths of the DPA. This effectively addresses unseen sources of nonlinearity by the theoretical model within the input signal bandwidth. Validation experiments were conducted on a 50W dual-input DPA designed for 5G wireless communication at 3.5 GHz, utilizing a 90 MHz modulated signal.
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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 | 0.000 |
| 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".