Complexity‐aware‐normalised mean squared error ‘CAN’ metric for dimension estimation of memory polynomial‐based power amplifiers behavioural models
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Bibliographic record
Abstract
The memory polynomial model is widely used for the behavioural modelling of radio‐frequency non‐linear power amplifiers having memory effects. One challenging task related to this model is the selection of its dimension which is defined by the non‐linearity order and the memory depth. This study presents an approach suitable for the selection of the model dimension in memory polynomial‐based power amplifiers’ behavioural models. The proposed approach uses a hybrid criterion that takes into account the model accuracy and its complexity. The proposed technique is tested on two memory polynomial‐based behavioural models. Experimental validation carried out using experimental data of two Doherty power amplifiers, built using different transistor technologies and tested with two different signals, illustrates consistent advantages of the proposed technique as it significantly reduces the model dimension by more than 60% without compromising its accuracy.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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