A Novel Digital Predistortion Coefficients Prediction Technique for Dynamic PA Nonlinearities Using Artificial Neural Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article presents a novel artificial neural network (ANN)-based digital predistortion (DPD) coefficients prediction (ANN-DPDCP) technique for dynamic nonlinearities induced by varying input power levels of power amplifiers (PAs). Conventional DPD techniques face challenges in mitigating dynamic nonlinearities efficiently. By modeling and predicting variations of conventional Volterra-based DPD coefficients using ANNs, the ANN-DPDCP technique rapidly provides appropriate DPD coefficients based on the target input power level. Benefiting from its concise training dataset and fitting capability, the ANN-DPDCP technique requires limited storage resources and derives DPD coefficients at arbitrary input power levels with negligible delay and comparable linearization performance. Experiments on a Ka-band PA driven by 100- and 400-MHz signals with a 12-dBm input power range illustrate storage resource reductions of 99.54% for 400 MHz and 99.81% for 100 MHz.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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