Adaptive principal component analysis for online reduced order parameter extraction in PA behavioral modeling and DPD linearization

Abstract

This paper presents a new method, based on the adaptive principal component analysis (APCA) technique, that iteratively creates and updates an orthogonal data matrix used to estimate the parameters of power amplifier (PA) behavioral models or digital predistortion (DPD) linearizers. Unlike the conventional PCA, the proposed block deflacted APCA (BD-APCA) is an iterative and online method that can be easily implemented in embedded processors. The proposed BD-APCA is designed by properly modifying the well-known complex domain generalized Hebbian algorithm (CGHA). This adaptation method enhances the robustness of the parameter estimation, simplifies the adaptation by reducing the number of estimated coefficients and due to the orthogonality of the new basis, these parameters can be estimated independently, thus allowing for scalability. Experimental results show that the proposed BD-APCA method is a worthy solution for adaptive, online, reduced-order and robust parameter estimation for PA modeling and DPD.