Mitigation of the impacts of the dynamic phase variation on the performance of GaN and LDMOS Doherty power amplifiers/transmitters
Bibliographic record
Abstract
To deal with the continuously increasing number of wireless communications subscribers and the growing quest for higher data rates, modern wireless communication standards (third generation and beyond) employ spectrum efficient modulation and access techniques, such as quadratic amplitude modulation (QAM) and orthogonal frequency division multiplexing (OFDM), etc. Although these techniques permit an efficient management of the overcrowded radio frequency (RF) spectrum, they also result in creating highly varying envelope signals that are characterized with high peak-to-average power ratio (PAPR). To avoid signal clipping and distortion of transmitted information, the power amplifier (PA) of wireless transmitter operates at large back-off from its saturation power level where its power efficiency drops drastically. The present work sheds the light on the phase imbalance problem and thoroughly analyses it for gallium nitride (GaN) and laterally diffused metal oxide semiconductor (LDMOS) device technologies, which are used in most of Doherty PA implementations. A number of strategies are then provided to tackle such a problem and to optimize the performance of GaN and LDMOS based Doherty PAs.
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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 | 0.001 |
| 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".