Investigation of Input-Output Waveform Engineered High-Efficiency Broadband Class B/J Power Amplifier
Bibliographic record
Abstract
This work introduces a new broadband Class B/J power amplifier (PA) design methodology by considering the input-output waveform shaping due to nonlinear CGS-VGS profile in gallium nitride (GaN) radio frequency (RF) devices. A comprehensive time domain modeling of drain voltage and drain current as a function of input nonlinearity is presented to predict continuous Class B/J PA performance. The proposed theory shows that continuous-mode operation can no longer maintain constant PA performance with varying fundamental and second harmonic impedances as predicted by the ideal class-J theory. This paper demonstrates that continuous-mode class B/J operation with input-output waveform shaping yields a new design space with two distinct regions: one advantageous region with capacitive second harmonic load terminations in which PA performance can be enhanced significantly; and another within an inductive second harmonic load area where the performance is degraded compared to that of a classical class B/J PA. For practical validation, source pull measurements and load pull analyses are presented and a broadband Class B/J PA is designed with the proposed second harmonic load and source termination. Source/Load-pull measurements/simulations confirm the theoretical predictions in terms of performance variation across the design space. A high efficiency design space is then identified for broadband PA design. The PA protype demonstrates drain efficiency of (60-73) % with output power above 40 dBm and corrected adjacent channel power ratio (ACPR) better than -55 dBc with digital predistortion (DPD) using 10 MHz LTE signal across (2.2-3.4) GHz.
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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) | 0.001 | 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".