Continual Transfer Learning Assisted Digital Predistortion for Dynamic Nonlinearities of Active Phased Arrays
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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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".