Machine Learning-Aided Piece-Wise Modeling Technique of Power Amplifier for Digital Predistortion
S. S. Krishna Chaitanya Bulusu, Nuutti Tervo, Praneeth Susarla, Mikko J. Sillanpää, Olli Silvén, Markku J. Juntti, Aarno Pärssinen
Abstract
We propose a new power amplifier (PA) behavioral modeling approach, to characterize and compensate for the signal quality degrading effects induced by a PA with a machine learning (ML) aided piece-wise (PW) modeling approach. Instead of using a single pruned Volterra model, we use multiple small-size pruned Volterra models by classifying the input data into different classes. For that purpose, an ML classifier model is trained by extracting some crucial features from both the input signal statistics and the PA operating point. The simulation results indicate that our approach contributes to an improved performance/complexity trade-off than a single generalized memory polynomial (GMP) model in terms of PA behavior modeling and linearization.
BibTeX
@inproceedings{icassp2023_machinelearninga,
title = {Machine Learning-Aided Piece-Wise Modeling Technique of Power Amplifier for Digital Predistortion},
author = {S. S. Krishna Chaitanya Bulusu and Nuutti Tervo and Praneeth Susarla and Mikko J. Sillanpää and Olli Silvén and Markku J. Juntti and Aarno Pärssinen},
booktitle = {ICASSP 2023},
year = {2023}
}