A Novel Weighted Memory Polynomial for Behavioral Modeling and Digital Predistortion of Nonlinear Wireless Transmitters
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Bibliographic record
Abstract
In this paper, a novel weighted memory polynomial (WMP)-based model is proposed for wireless transmitters and radio-frequency power amplifiers' (PAs) behavioral modeling and predistortion. The new model introduces an instantaneous-power-dependent weight function on the static and dynamic terms of the conventional memory polynomial (MP) model. Experimental validation in both modeling and predistortion contexts was performed on a PA prototype driven by a 20-MHz Long Term Evolution signal. The proposed model was assessed against the standard MP model. The experimental results demonstrate the superiority of the proposed polynomial in behavioral modeling applications as it results in up to 50% (3-dB) improvement in the normalized mean square error for the same number of coefficients. The model robustness was then validated by using a second test signal applied to two Doherty PAs using different transistor technologies. Furthermore, when applied for digital predistortion, the proposed WMP function achieves the same performance as the state-of-art MP while requiring approximately 50% less coefficients.
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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