A dynamic coefficient polynomial predistorter based on direct learning architecture  

作　　者：李波 Ge Jianhua Ai Bo 

机构地区：[1]State Key Laboratory of Integrated Service Networks, Xidian University, Xi＇an 710071, P. R. China [2]State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, P.R. China

出　　处：《High Technology Letters》2008年第2期118-122,共5页高技术通讯（英文版）

基　　金：the National High Technology Research and Development Programme of China(No2006AA01Z270);Beijing Jiaotong University Talent Foundation(No2007RC022)

摘　　要：A dynamic coefficient polynomial predistorter based on direct learning architecture is proposed.Compared to the existing polynomial predistorter,on the one hand,the proposed predistorter based on thedirect learning architecture is more robust to initial conditions of the tap coefficients than that based on in-direct learning architecture;on the other hand,by using two polynomial coefficient combinations,differ-ent polynomial coefficient combination can be selected when the input signal amplitude changes,whicheffectively decreases the estimate error.This paper introduces the direct learning architecture and givesthe dynamic coefficient polynomial expression.A simplified nonlinear recursive least-squares(RLS)algo-rithm for polynomial coefficient estimation is also derived in detail.Computer simulations show that theproposed predistorter can attain 31 dB,28dB and 40dB spectrum suppression gain when our method is ap-plied to the traveling wave tube amplifier(TWTA),solid state power amplifier(SSPA)and polynomialpower amplifier(PA)model,respectively.A dynamic coefficient polynomial predistorter based on direct learning architecture is proposed. Compared to the existing polynomial predistorter, on the one hand, the proposed predistorter based on the direct learning architecture is more robust to initial conditions of the tap coefficients than that based on indirect learning architecture; on the other hand, by using two polynomial coefficient combinations, different polynomial coefficient combination can be selected when the input signal amplitude changes, which effectively decreases the estimate error. This paper introduces the direct learning architecture and gives the dynamic coefficient polynomial expression. A simplified nonlinear recursive least-squares （RLS） algo- rithm for polynomial coefficient estimation is also derived in detail. Computer simulations show that the proposed predistorter can attain 31dB, 28dB and 40dB spectrum suppression gain when our method is applied to the traveling wave tube amplifier （TWTA）, solid state power amplifier （SSPA） and polynomial power amplifier （PA） model, respectively.

关 键 词：orthogonal frequency division multiplexing （OFDM） PREDISTORTION POLYNOMIAL recur-sive least-squares （RLS） 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]