Using recurrent neural network for adaptive predistortion linearization of RF amplifiers

Abstract

A novel adaptive predistorter for linearizing a power amplifier in a mobile transmitter is studied. Unlike most other predistorters reported in the literatures, this predistorter is constructed as a complex‐valued recurrent neural network (RNN). The weights of the RNN were adjusted by using complex real time recurrent learning (RTRL) algorithm. Thus the AM/AM and AM/PM responses of the proposed predistorter are simultaneously implemented. The proposed scheme is shown to attain superior performance in comparison with other most well‐known predistortion structures. The performance of the proposed predistorter is demonstrated through computer simulations. © 2002 John Wiley & Sons, Inc. Int J RF and Microwave CAE 12: 125–130, 2002.