Early stopping criteria for adaptive training of dynamic nonlinear behavioural models

Abstract

As the physical makeup of cellular basestations evolve into systems with multiple parallel transmission paths the effort involved in modelling these complex systems increases considerably. One task in particular which contributes to signal distortion on each signal path, is the power amplifier. In power amplifier (PA) modelling, Recursive Least Squares (RLS) has been used in the past to train Volterra models with memory terms. The Volterra model is widely used for modelling of PAs. In this paper we present a comparison of the stability performance for a PA model during training for various model memory lengths, model orders of non linearity and signal sample rates. This examination provides a technique to avoid instability occurring during the adaptive training of dynamic nonlinear behavioural models.