A Direct Learning Digital Predistortion Algorithm to Accurately Compensate for the Power Amplifier Nonlinearity

Abstract

The conventional digital predistortion (DPD) algorithm based on model identification involves approximations, which introduce an ineligible calculation error, and hence cause performance degradation. In this paper, we present a novel direct learning predistortion algorithm which identifies the power amplifier (PA) model using the least square (LS) algorithm and accurately calculates the predistortion function by a new method. The method constructs a univariate polynomial and finds its roots to obtain the accurate value of the DPD function. Although the proposed algorithm will require additional calculations, it can compensate the PA nonlinearity more precisely. Simulation results demonstrate that the new algorithm outperforms the conventional algorithm in both the adjacent channel leakage ratio (ACLR) and normalized mean square error (NMSE) performance.