Work

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

Year: 2024

Type: article

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.

Source: IEEE Microwave and Wireless Technology Letters

DOI: 10.1109/lmwt.2024.3393859

Authors: Arne Fischer-Bühner, Lauri Anttila, Manil Dev Gomony, Mikko Valkama

Institutions: Tampere University, Nokia (Belgium), University of Tampere, Eindhoven University of Technology

Language: English

Topic: Advanced Power Amplifier Design

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,495

Funder: Academy of Finland