Cascade Structure of Digital Predistorter for Power Amplifier Linearization

Abstract

In this paper, a cascade structure of nonlinear digital predistorter (DPD) synthesized by the direct learning adaptive algorithm is represented. DPD is used for linearization of power amplifier (PA) characteristic, namely for compensation of PA nonlinear distortion. Blocks of the cascade DPD are described by different models: the functional link artificial neural network (FLANN), the polynomial perceptron network (PPN) and the radially pruned Volterra model (RPVM). At synthesis of the cascade DPD there is possibility to overcome the ill conditionality problem due to reducing the dimension of DPD nonlinear operator approximation. Results of compensating nonlinear distortion in Wiener–Hammerstein model of PA at the GSM–signal with four carriers are shown. The highest accuracy of PA linearization is produced by the cascade DPD containing PPN and RPVM.