A Mutual Distortion and Impairment Compensator for Wideband Direct-Conversion Transmitters Using Neural Networks

Abstract

This paper presents a one-step solution for transmitter nonlinearity estimation and linearization control in the presence of I/Q modulator imperfections for wideband direct-conversion transmitters. These transmitters include power amplifiers with frequency-dependent nonlinearities and modulator imperfections. With the proposed two-hidden-layer feedforward neural network, traditional two-step characterization and specially designed training signals are not required in the parameter estimation stage; and, estimation can be done without interrupting the operation of the transmitter. The measurement results and comparisons of the proposed neural network with the existing state-of-the-art methods show the superior performance in the presence of extreme RF impairments.