This paper describes an adaptive digital predistorter (ADP) for RF power amplifier (PA) linearization using an adaptive neuro-fuzzy inference system (ANFIS). The ANFIS predistorter (PD) employs the advantage of real-time modeling of the PA's responses in determining the PD's functions. The amplitude and phase corrections for the PD are represented in an easy-to-understand fuzzy if-then rule, while the parameters involved in the fuzzy representation are trained using neural networks algorithms, namely gradient-descent and least squares estimate (LSE). Experimental results show that a 26.3-dB improvement in linearity for a two-tone signal is obtained, while a distorted WCDMA signal is suppressed by at least 12 dB. The adaptability of the ANFIS PD to instantaneous variation in PA responses through time is also demonstrated, and results show that the ANFIS PD is capable of adapting to simulated environmental changes, which is a topic often omitted by researchers in this area. Further testing demonstrated that the tuning parameters involved in the training could be reduced by more than half for a fairly nonlinear PA without significantly degrading the suppression capability.
['Kok Chew Lee', 'P. Gardner']
Adaptive neuro-fuzzy inference system (ANFIS) digital predistorter for RF power amplifier linearization