基于偏最小二乘的低复杂度数字预失真  

Low Computational Complexity Digital Predistortion Based on Partial Least Squares

作　　者：林鑫 刘发林[1,2] 李泓旻[1,2] 李刚 张益康[1,2] LIN Xin;LIU Fa-lin;LI Hong-min;LI Gang;ZHANG Yi-kang(Department of Electronic Engineering and Information Science,University of Science and Technology of China,Hefei 230027,China;Key Laboratory of Electromagnetic Space Information,Chinese Academy of Sciences,Hefei 230027,China)

机构地区：[1]中国科学技术大学电子工程与信息科学系,合肥230027 [2]中国科学院电磁空间信息重点实验室,合肥230027

出　　处：《微波学报》2021年第1期64-69,共6页Journal of Microwaves

基　　金：国家自然科学基金(61471333)。

摘　　要：在宽带场景下,传统的数字预失真(DPD)模型需要更高的阶次和更多的系数来校正功率放大器(PA)强非线性和记忆效应,这就会导致极高的计算复杂度和解算系数时的病态问题。文章围绕复杂DPD模型参数辨识和低复杂度DPD算法实现展开深入研究,提出了一种新的基于偏最小二乘(PLS)的低复杂度DPD方法。所提方法根据PA的前逆输入输出特性来获得常数转换矩阵,生成新的基函数矩阵实现模型系数降维。与现有的DPD系数降维方法相比,该方法能在几乎不损失性能的情况下大幅减少DPD模型系数维度和计算复杂度。实验验证了新方法能大幅降低复杂度且具有很好的线性化性能。In wideband applications,the number of coefficients in conventional digital predistortion(DPD)models increases prominently to compensate the high nonlinearity and deep memory effect of the PA.And the increase of the coefficients leads to very high computational complexity and ill-posed problems in the process of extracting the DPD parameters.This paper focuses on the parameter identification of complex DPD models and the implementation of low computational complexity DPD algorithms.A novel low complexity DPD method is proposed based on partial least squares(PLS).This method obtains new basis function matrices based on input/output characteristics of a PA′s pre-inverse to reduce the number of coefficients.Compared with the existing dimension-reduction based DPD methods,this method further reduces both the number of coefficients in DPD models and the computational complexity with almost negligible performance loss.Experimental results demonstrate that the new method can greatly reduce complexity with good linearization.

关 键 词：数字预失真 宽带 功率放大器 偏最小二乘 

分 类 号：TN929.5[电子电信—通信与信息系统]