Experimental demonstration of a machine learning-based piece-wise digital predistortion method in 5G NR systems

Bulusu, S. S. Krishna Chaitanya; Khan, Bilal; Tervo, Nuutti; Leinonen, Marko E.; Sillanpää, Mikko J.; Silvén, Olli; Juntti, Markku; Pärssinen, Aarno (2023-07-28)
 
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https://doi.org/10.1109/IMS37964.2023.10188119

Bulusu, S. S. Krishna Chaitanya
Khan, Bilal
Tervo, Nuutti
Leinonen, Marko E.
Sillanpää, Mikko J.
Silvén, Olli
Juntti, Markku
Pärssinen, Aarno
Institute of Electrical and Electronic Engineers
28.07.2023

S. S. K. C. Bulusu et al., "Experimental Demonstration of a Machine Learning-based Piece-wise Digital Predistortion Method in 5G NR systems," 2023 IEEE/MTT-S International Microwave Symposium - IMS 2023, San Diego, CA, USA, 2023, pp. 81-84, doi: 10.1109/IMS37964.2023.10188119

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doi:https://doi.org/10.1109/IMS37964.2023.10188119
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https://urn.fi/URN:NBN:fi-fe20230830113360
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Abstract

This paper demonstrates a piece-wise digital predistortion (PW-DPD) for a power amplifier (PA) in 5G new radio (NR) systems. It involves modeling the digital predistorter based on the machine learning (ML) classification of the operational states. The experimental results demonstrate that by extracting some key features from 5G NR signal statistics and the PA operating point can offer better PA linearization performance/complexity tradeoff than the conventional approach based on a single pruned Volterra model. The proposed approach is validated by laboratory experiments and shows up to 3.5 dB error vector magnitude (EVM) improvement over the conventional approach for a class A PA at 28 GHz.

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