An Accurate Complexity-Reduced “PLUME” Model for Behavioral Modeling and Digital Predistortion of RF Power Amplifiers
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Bibliographic record
Abstract
This paper introduces a new, accurate, and complexity-reduced three-nonlinear-box model that is suitable for the behavioral modeling and digital predistortion (DPD) of power amplifiers (PAs) exhibiting memory effects. This model is composed of a look-up table (LUT), a memory polynomial (MP), and an envelope MP (EMP), which are all connected in parallel, and it is termed as Parallel-LUT-MP-EMP (PLUME). The PLUME model's performance is experimentally assessed using a highly nonlinear Doherty PA driven by a multicarrier wideband code division multiple access signal. A comparison is held between the PLUME model and different state-of-the-art models reported in the literature, such as the MP model, the parallel twin nonlinear two-box model, and the generalized MP (GMP) model. The experimental results, in both behavioral modeling and DPD applications, demonstrate that the proposed PLUME model outperforms the first two models. However, it shows the same accuracy as the GMP model but with an approximately 45% reduction in the number of coefficients. This significant decrease in coefficients considerably reduces the model computational complexity. Another comparison of the resources utilized for field programmable gate array implementation of the PLUME model and the GMP model is performed, which reveals that the PLUME model uses much fewer resources than the other model.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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