Robust power amplifier predistorter by using memory polynomials  

作　　者：Li Bo Ge Jianhua Ai Bo 

机构地区：[1]State Key Lab. of Integrated Service Networks, Xidian Univ., Xi'an 710071, P. R. China [2]School of Information Engineering, Chang'an Univ., Xi'an 710064, P. R. China [3]Engineering Coll. of Armed Police Force, Xi'an 710086, P. R. China

出　　处：《Journal of Systems Engineering and Electronics》2009年第4期700-705,共6页系统工程与电子技术（英文版）

基　　金：supported by the National High Technology Research and Development Program of China(2006AA01Z270).

摘　　要：In memory polynomial predistorter design, the coefficient estimation algorithm based on normalized least mean square is sensitive to initialization parameters. A predistorter based on generalized normalized gradient descent algorithm is proposed. The merit of the GNGD algorithm is that its learning rate provides compensation for the independent assumptions in the derivation of NLMS, thus its stability is improved. Computer simulation shows that the proposed predistorter is very robust. It can overcome the sensitivity of initialization parameters and get a better linearization performance.In memory polynomial predistorter design, the coefficient estimation algorithm based on normalized least mean square is sensitive to initialization parameters. A predistorter based on generalized normalized gradient descent algorithm is proposed. The merit of the GNGD algorithm is that its learning rate provides compensation for the independent assumptions in the derivation of NLMS, thus its stability is improved. Computer simulation shows that the proposed predistorter is very robust. It can overcome the sensitivity of initialization parameters and get a better linearization performance.

关 键 词：power amplifier predistortion memory polynomial generalized normalized gradient descent orthogonal frequency division multiplexing. 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]