Bandwidth Performance Analysis of DLM PAs Including the Class-A/B/J Continuum
Bibliographic record
Abstract
In this paper, a new method is presented to analyze the bandwidth performance of power amplifiers (PAs) including the Class-A/B/J Continuum for dynamic load modulation (DLM). Due to the introduced biasing operation factor p, the analysis of bandwidth performance of DLM Class-J PAs can be extended to other modes, such as deep Class-AB mode. With the help of MATLAB software, the relationship between the drain efficiency (η) and output power back-off (OPBO) at the current generator plane (CGP) is demonstrated clearly. At the CGP, the proposed method indicates that when p = 0, the fractional bandwidth (FBW) of DLM PAs is improved from 73% to 141% compared with the results at the extrinsic plane (EP). When p is increased to 0.2, the FBW of DLM PAs can be improved from 141% to 156%. Furthermore, the proposed method in this work provides clear guideline to do a general analysis on the bandwidth performance of DLM PAs including the Class-A/B/J Continuum, and it also provides potential theoretical guidance for engineers to design wideband DLM 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".