Efficient Digital Signal Recovery for Satellite Communications With Active Phased Arrays

Abstract

In this letter, a transfer learning-assisted (TLA) digital signal recovery (DSR) method is proposed for the linearization of active phased arrays (APAs) for satellite communications to handle the transition to different communication scenarios. The frozen layers of a deep neural network (DNN) are pretrained to extract the main nonlinear characteristics of the APA, and the fine-tuning layers are adopted to achieve prompt update of the regression parameters according to the variations in average input power levels and steering angles. This allows satisfactory reception performance to be maintained in time when the nonlinearity of the APA changes. Experimental results on a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4 \times 4$</tex-math> </inline-formula> APA antenna at 28 GHz show that the proposed method improves the adjacent channel power ratio (ACPR) and the error vector magnitude (EVM) performance. Compared with the existing fully relearned DNN-DSR method, the proposed method achieves almost as good performance with only 1/10 of the data.