Continual Transfer Learning Assisted Digital Predistortion for Dynamic Nonlinearities of Active Phased Arrays

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).