Signal Reconstruction Deep Residual Neural Network-Based Bandwidth Augmented Methods for DPD Linearization

Abstract

In this letter, a signal reconstruction deep residual neural network (SRDRNN)-based bandwidth augmented method for digital predistortion (DPD) linearization of a radio frequency (RF) power amplifier (PA) is proposed. The SRDRNN is utilized to interpolate the samples from the analog-to-digital (ADC) converter in the DPD feedback path of the RF PA so as to increase its bandwidth. The experimental results illustrate that the SRDRNN can accurately produce the out-of-band spectrum regrowth of the equivalent baseband signal for a PA. It can also be directly applied to another PA with the same type. Finally, the effectiveness of SRDRNN-based bandwidth augmented method is verified with DPD linearization experiment. Both of the feedback signals reconstructed by the SRDRNN for the training PA and the test PA exhibit an excellent linearization performance.