Machine learning-aided piece-wise modeling technique of power amplifier for digital predistortion

Bulusu, S. S. Krishna Chaitanya; Tervo, Nuutti; Susarla, Praneeth; Sillanpää, Mikko J.; Silvén, Olli; Juntti, Markku; Pärssinen, Aarno (2023-05-05)
 
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https://doi.org/10.1109/ICASSP49357.2023.10096989

Bulusu, S. S. Krishna Chaitanya
Tervo, Nuutti
Susarla, Praneeth
Sillanpää, Mikko J.
Silvén, Olli
Juntti, Markku
Pärssinen, Aarno
Institute of Electrical and Electronics Engineers
05.05.2023

S. S. Krishna Chaitanya Bulusu et al., "Machine Learning-Aided Piece-Wise Modeling Technique of Power Amplifier for Digital Predistortion," ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece, 2023, pp. 1-5, doi: 10.1109/ICASSP49357.2023.10096989

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doi:https://doi.org/10.1109/ICASSP49357.2023.10096989
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https://urn.fi/URN:NBN:fi-fe2023070690326
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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.

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