An Analysis of Adaptive Digital Predistortion Algorithms in RF Power Amplifier

International audienceThis paper develops an adaptive Indirect LearningArchitecture (ILA) using either Least Mean Square (LMS)or Recursive Least Square (RLS) in digital predistortion forradio frequency power amplifiers. The adaptive ILA enablesto update the coefficient fluctuation of the predistorter as thenonlinear behavior of the RF PA characteristics changes due tothe Temperature, Voltage, Aging (TVA) drift and other factors.The performance efficiency of the offline Linear InterpolatedLook-Up-Table (LILUT) based DLA is analyzed as the PAcharacteristics change due to the temperature. The experimentalresults show that the adaptive ILA is able to be more effectivelycharacterizing the nonlinear behavior change of the PA characteristicsinduced by the TVA drift. Moreover, in order to getmore linear performance, the offline LILUT-based DLA must bere-designed with respect to each PA model change