An adaptive predistorter using modified neural networks combined with a fuzzy controller for nonlinear power amplifiers

In digital radio systems, high data transmission rates require the use of spectrally efficient linear modulation techniques; however, these techniques are generally sensitive to nonlinearity caused by the high-power amplifier (HPA) employed in transmitter systems. The nonlinearity of HPA is potentially responsible for spectral spreading, adjacent channel interference (ACI), and degradation of bit-error rates (BERs). This article proposes an adaptive predistortion scheme to compensate for the HPA's nonlinearity by combining adaptive structure-varying neural networks and a fuzzy controller. Simulations show that this predistortion scheme can very effectively prevent the warping of the signal constellations, thus reducing the system's BER and learning time. © 2003 Wiley Periodicals, Inc. Int J RF and Microwave CAE 14: 15–20, 2004.