A Piecewise Interpolation Based Digital Predistortion of Power Amplifiers Across Wide Power Ranges
Bibliographic record
Abstract
This paper presents a novel piecewise linear interpolation scheme to enable digital predistortion (DPD) for linearizing power amplifiers (PAs) across the wide power ranges required in massive multipleinput multiple-output (mMIMO) transmitters. By interpolating between DPD coefficients corresponding to a subset of average power levels, the proposed method reduces linearization complexity and eliminates the need for frequent DPD retraining at every power operating point. Theoretical derivations of the interpolation scheme are provided, followed by experimental validation on a Gallium Nitride (GaN) PA operating at 13 GHz. The results show that interpolating DPD coefficients at four different power levels over a 17 dB range achieved an Adjacent Channel Power Ratio (ACPR) and Error Vector Magnitude (EVM) of better than -44 dB and -38 dB, respectively. This provides a favorable balance between dedicated DPD training at each power level (ACPR and EVM of better than -47 dB and -42 dB) and using a single DPD set at peak power (ACPR and EVM of better than -37 dB and -28 dB). The proposed approach offers an efficient solution for scaling DPD with power while maintaining acceptable linearity.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".