A Residual-Fitting Modeling Method for Digital Predistortion of Broadband Power Amplifiers
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Bibliographic record
Abstract
This letter proposes a residual-fitting modeling method for digital predistortion (DPD) of broadband power amplifiers (PAs), and then constructs a residual-fitting model. To avoid directly modeling strong nonlinearity and memory effect, the model is split into a conversion, fitting, and recovery module. In this way, the nonlinearity and memory effect of the output signal of PAs are reduced after the conversion module, and then the fitting module models the converted signal, finally the behavioral characteristics of PAs are recovered by the recovery module. In the experimental test, a 100 MHz orthogonal frequency division multiplexing (OFDM) signal is used as input signal of a Doherty PA. The experimental results show that compared with the existing augmented real-valued time-delay neural network (ARVTDNN), the proposed residual-fitting memory polynomial-ARVTDNN (MP-ARVTDNN) model with much fewer coefficients lowers normalized mean square error (NMSE) and adjacent channel power ratio (ACPR).
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 0.000 |
Machine scores (provisional)
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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 it