First-pass design of high efficiency power amplifiers using accurate large signal models
Bibliographic record
Abstract
A systematic, first-pass methodology for designing high efficiency power amplifier (PA) using only large signal CAD models is presented. Detailed analysis using the model reveals significant insights into PA operation as well as the required impedance environment for high efficiency mode of operation. In particular, waveform engineering and empirical loadpull are used to determine the optimal class of operation and impedance terminations. Combined with the use of precise electromagnetic simulator in synthesizing the matching network, first pass design of a 10W, 3.3 GHz GaN inverse class F PA as well as a 2.5 GHz push-pull inverse class F PA was realized with very good agreement between simulation and measurement results. Specifically, the 3.3 GHz PA achieved 74% power added efficiency (PAE) at 3.27 GHz with 38.27 dBm output power, while the push-pull PA achieved 75% drain efficiency with 42.7 dBm output power. The linearizability of the 3.3 GHz PA is demonstrated using predistorted WiMAX modulated signals. When combined with DPD, the PA showed acceptable EVM for use in next generation wireless base stations.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".