An Analysis of Adaptive Digital Predistortion Algorithms in RF Power Amplifier

This paper develops an adaptive Indirect Learning Architecture (ILA) using either Least Mean Square (LMS) or Recursive Least Square (RLS) in digital predistortion for radio frequency power amplifiers. The adaptive ILA enables to update the coefficient fluctuation of the predistorter as the nonlinear behavior of the RF PA characteristics changes due to the Temperature, Voltage, Aging (TVA) drift and other factors. The performance efficiency of the offline Linear Interpolated Look-Up-Table (LILUT) based D LA is analyzed as the PA characteristics change due to the temperature. The experimental results show that the adaptive ILA is able to be more effectively characterizing the nonlinear behavior change of the PA characteristics induced by the TVA drift. Moreover, in order to get more linear performance, the offline LILUT-based DLA must be re-designed with respect to each PA model change.