Title :
Neural-network-based adaptive baseband predistortion method for RF power amplifiers
Author :
Naskas, N. ; Papananos, Y.
Author_Institution :
Sch. of Electr. & Comput. Eng., Nat. Tech. Univ. of Athens, Zografou, Greece
Abstract :
An adaptive baseband predistortion method for RF power amplifier (PA) linearization is proposed and experimentally demonstrated. The predistortion component is implemented by a single-input dual-output multilayer perceptron (MLP). Both amplitude-to-amplitude and amplitude-to-phase distortion products are compensated by backpropagation training of the neural network including the response of the PA. Effects of modulator and demodulator imperfections on system performance are examined. Measurements on a system prototype reveal a significant linearity improvement that reaches 25 dB.
Keywords :
adaptive signal processing; backpropagation; distortion; linearisation techniques; multilayer perceptrons; power amplifiers; radiofrequency amplifiers; RF power amplifiers; adaptive baseband predistortion; amplitude-to-amplitude distortion; amplitude-to-phase distortion; backpropagation training; multilayer perceptron; neural network; Amplitude modulation; Backpropagation; Baseband; Demodulation; Multilayer perceptrons; Neural networks; Power amplifiers; Predistortion; Radio frequency; Radiofrequency amplifiers; 65; Baseband predistortion; MLP; NN; PA; multilayer perceptron; neural network; power amplifier;
Journal_Title :
Circuits and Systems II: Express Briefs, IEEE Transactions on
DOI :
10.1109/TCSII.2004.837284
