On the Robustness of Digital Predistortion Function Synthesis and Average Power Tracking for Highly Nonlinear Power Amplifiers
Bibliographic record
Abstract
In this paper, a comprehensive study of the robustness of the digital predistortion function synthesis is presented. This study covers two aspects: the processing of the power amplifier (PA) input and output measured data intended for the extraction of the corresponding predistortion function, and the optimal setting of the predistorter's small-signal gain to adaptively track the average power variation between the input and output of the pre-distorter. First, the accuracy of the polynomial curve fitting and the lookup table's scheme in mimicking the measured AM/AM and AM/PM characteristics of the PA is investigated. To address the high dispersion of coefficients of the polynomial function, which limits the order that can be implemented and the fitting capabilities, a pre-processing technique is proposed. Second, the fitted PA curves are used to investigate the effects of the small-signal gain of the predistortion function on the linearization performance. An automated average power tracking technique is introduced in order to maintain a unit average gain of the predistorter. The measured spectra at the output of the amplifier show an additional 12-dBc improvement in the output spectrum regrowth.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".