A Novel Scheme for Power Amplifier Predistortion Based on Indirect Learning Architecture

Abstract

Polynomial predistortion techniques for power amplifier (PA) non-linearities based on indirect learning architecture (IDLA) are widely used. The benefit of the IDLA leaves unnecessary the assumption of a model for PA, corresponding parameters estimation and inverse construction. In this paper, a novel scheme based on IDLA for PA predistortion is proposed. It has better power spectral density (PSD) and relative mean square error (RMSE) performances than the conventional IDLA-based methods especially when the PA non-liearity is more severe. Simulations and theoretical analysis verify the good performances of the proposed scheme.