Efficiency and Bandwidth Improvement of Digitally-Assisted LMBAs Using Adaptive Input Power Distribution
Bibliographic record
Abstract
This paper introduces a frequency-dependent power-splitting technique to enhance power-added efficiency (PAE) and bandwidth in digitally-assisted load-modulated balanced amplifiers (LMBAs). The splitting function for the injection signal is optimized using a Genetic Algorithm (NSGA-II) based on modulated signal measurements within the LMBA's 3-dB bandwidth. Testing with two QPA2935 amplifiers shows a nearly 140 MHz bandwidth extension with no loss in linearity or efficiency. PAE improvement varies across the bandwidth, reaching over 15%. Assessment with a 50 MHz 64-QAM signal shows PAE improvements of 14.85% and 4.79% at 3.025 and 3.525 GHz, respectively, with significant frequency response flatness.
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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".