Cancellation of nonlinear distortion based on integration of FCM clustering algorithm and adaptive-two-stage linear approximation

A hybrid system of the fuzzy c-means (FCM) clustering algorithm and adaptive-two-stage linear approximation was presented for nonlinear distortion cancellation of radio frequency (RF) power amplifier (PA). This mechanism can effectively eliminate noise, adaptively model PA's instantaneous change, and efficiently correct nonlinear distortion. This article puts forward the FCM clustering algorithm for clustering received signals to eliminate white noise, and then uses the adaptive-two-stage linear approximation to fit the inverse function of the amplitude's and phase's nonlinear mapping during the training phase. Parameters of the linear function and similarity function are trained using the gradient-descent and minimum mean-square error criteria. The proposed approach's training results is directly employed to eliminate sampling signal's nonlinear distortion. This hybrid method is realized easier than the multi-segment linear approximation and could reduce the received signal's bit error rate (BER) more efficiently.