Optimization of feedforward amplifier power efficiency on the basis of drive statistics
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Bibliographic record
Abstract
Among power amplifier linearization techniques, feedforward delivers the best performance, but at the cost of significant degradation in the amplifier's power efficiency. This paper details a procedure to design feedforward amplifiers for optimal DC-RF conversion efficiency. The procedure has the convenience of requiring only the power statistics of the driving signal, of being computationally efficient, and of lending itself to a highly intuitive graphical representation. The gains and normalized saturated output powers of the main and error amplifiers, as well as the various couplings, are optimized for a specified linearity and gain at the output of the system. The amplifier types and adaptation methods employed must be specified at the outset, but results are presented that begin to reveal the impact of these factors on efficiency and, thus, to demonstrate the investigative potential of the procedure.
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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 it