Synergetic Crest Factor Reduction and Baseband Digital Predistortion for Adaptive 3G Doherty Power Amplifier Linearizer Design
Bibliographic record
Abstract
A novel approach for power amplifier (PA) characterization suitable for single iteration digital predistorter synthesis is proposed. This approach consists of synergetic crest factor reduction and baseband digital predistortion to avoid the average power variation at the input of the PA between the characterization and linearization steps. This is achieved by bypassing the crest factor reduction block during the characterization step and by applying it concurrently with digital predistortion in the linearization step. First, the PA's behavior sensitivity to the average input power is evaluated. The limitations of conventional approaches for the PA characterization, in the context of single iteration digital predistortion, are then demonstrated. The performance of the proposed technique is validated experimentally on a 300-W peak PA. The measured improvement of the adjacent channel power ratio at the output of the linearized amplifier is 16 dBc for the conventional approach and 29 dBc for the proposed approach.
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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".