An Improved Direct Learning Architecture for Satellite High Power Amplifiers Pre-Distortion

Abstract

The pre-distortion techniques based on traditional direct learning architecture (DLA) are commonly used to linearize high power amplifiers (HPAs) settled in ground-station or on board satellite merely, which cannot achieve a satisfying linearization. In this paper, an improved DLA, considering the satellite channel features with two HPAs in the loop, is put forward. Nonlinear polynomial with 7 order and 4 memory depth is utilized as pre-distorter and recursive least square (RLS) algorithm is adopted for its parameters updating. Simulation results show that with pre-distortion the power spectrum density (PSD) of 16APSK signal achieves about 7.2dB improvement, the EVM of signal constellation is greatly improved up to 61.83%, and desirable system linearization performance is acquired.