Efficiency and Bandwidth Extension of Dual-Input Doherty Amplifiers Using Frequency-Domain Adaptive Input Power Distribution
Bibliographic record
Abstract
Phase adjustment and uneven power-splitting are two common methods of improving the efficiency and bandwidth of dual-input Doherty Power Amplifiers (DPAs), also known as Digital Doherty PAs (DDPAs). In this paper, we propose a frequency-dependent input power-splitting technique that improves the power-added efficiency (PAE) and bandwidth of the DDPA while maintaining a constant linearity performance compared to analog (or conventional) Doherty mode. The proposed splitting functions are obtained from the measurements conducted using a multi-tone signal and optimum power-splitting factors extracted using a Sorting Genetic Algorithm (NSGA-II) at each frequency within the 3-dB bandwidth of the DDPA. Multi-tone measurements were performed using a gallium-nitride (GaN)-based DDPA, demonstrating an extension of the bandwidth by almost 110 MHz without any penalty to linearity or efficiency. The PAE improvement, however, varied across the DDPA’s bandwidth, ranging from 2.25% to 8.15%. The performance of the splitting functions was then assessed using a 50 MHz 64-QAM modulated signal centered at 1.95 and 2.2 GHz. The results illustrated that the adjacent-channel power ratio remained below those measured under conventional Doherty mode. Furthermore, PAE improvements of 2.85% and 7.88% were achieved at 1.95 and 2.2 GHz, respectively, while substantial gain flatness improvement was attained.
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) | 0.000 | 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 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".