An Accurate Complexity-Reduced “PLUME” Model for Behavioral Modeling and Digital Predistortion of RF Power Amplifiers
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".