Recursive Neural Network with Phase-Normalization for Modeling and Linearization of RF Power Amplifiers

Abstract

This letter presents a novel phase-normalized recurrent neural network (PN-RNN) to linearize radio frequency (RF) power amplifiers (PAs) in high-bandwidth communication systems with significant memory effects. The proposed approach builds on proper phase alignment of the internal hidden variables in the recursive processing system. The provided RF measurement-based modeling and digital predistortion (DPD) results at 1.8 and 3.5 GHz demonstrate a significantly improved modeling capacity and predistortion ability when applying phase normalization, confirming the validity of the proposed approach.