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.
WANG Gui-yeZOU Wei-xiaWANG Zhen-yuDU Guang-longGAO Ying
Punctured convolution codes （PCCs） have a lot of applications in modem communication system. The efficient way to search for best PCCs with longer constraint lengths is desired since the complexity of exhaustive search becomes unacceptable. An efficient search method to find PCCs is proposed and simulated. At first, PCCs＇ searching problem is turned into an optimization problem through analysis of PCCs＇ judging criteria, and the inefficiency to use pattern search （PS） for many local optimums is pointed out. The simulated annealing （SA） is adapted to the non-convex optimization problem to find best PCCs with low complexity. Simulation indicates that SA performs very well both in complexity and success ratio, and PCCs with memories varying from 9 to 12 and rates varying from 2/3 to 4/5 searched by SA are presented.
ZOU Wei-xiaWANG Zhen-yuWANG Gui-yeDU Guang-longGAO Ying
