Three-Layered Biased Memory Polynomial for Dynamic Modeling and Predistortion of Transmitters With Memory
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Bibliographic record
Abstract
This paper proposes a new three-layered biased memory polynomial for behavioral modeling and digital predistortion of highly nonlinear transmitters/power amplifiers (PAs) for 3G wireless applications. The proposed model considers the possibility that the nonlinearity order of the dynamic part of the PA characteristics is different from the nonlinearity order of the static part. For highly nonlinear PAs, the proposed model offers some benefits, such as a low dispersion of coefficients, numerical stability and a low number of coefficients. Moreover, with the measurement setup, better in-band performance is reported. To establish the performance of the linearized PA under realistic conditions, experiments have been carried out for a deep biased class-AB PA and a Doherty PA for various modeling and signal quality norms defined for 3G signals.
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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)
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 it