Sample Selection Method for Digital Predistortion with Incremental Dimension of Coefficients

In this paper, we present a new method for the linearization of radio frequency (RF) power amplifies (PAs) based on the sample selection method (SSM) with incremental dimension of coefficients. In SSM, insufficient training samples can cause some problems such as the low convergence speed and serious noise interference. In order to solve these problems, we propose to use coefficient vectors with increasing dimension in iterations and then to introduce the dimension reduction (or pruning) technique to ensure the priority selections of important coefficients in training. Experimental results show that the proposed method can achieve better normalized mean square error (NMSE) and adjacent channel power ratio (ACPR) with faster convergence speed compared with SSM.