Continual Transfer Learning Assisted Digital Predistortion for Dynamic Nonlinearities of Active Phased Arrays
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Bibliographic record
Abstract
This letter presents a continual transfer learning-assisted (CTLA) digital predistortion (DPD) (CTLA-DPD) method for linearizing active phased arrays (APAs) with dynamic nonlinearities. Unlike existing transfer learning-assisted (TLA) DPD methods, the proposed method does not rely on a specific reference operating state. Instead, whenever the working conditions of the APA are updated, deep neural networks (DNNs) utilized in the previous state serve as the initial model to initiate a new training process until the retrained DNN effectively adapts to the new operating state. Experiments are conducted on an APA in the 28 GHz band with dynamic configurations including bandwidth, average input power level, and beam steering angle. The experimental results show that the proposed method can reduce the required data amount for the online transfer learning phase by 60% and achieve performance improvements in adjacent channel power ratio (ACPR) and normalized mean squared error (NMSE).
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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