University of Oulu

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

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

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Author: Bulusu, S. S. Krishna Chaitanya1; Khan, Bilal1; Tervo, Nuutti1;
Organizations: 1University of Oulu, Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 1.8 MB)
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Language: English
Published: Institute of Electrical and Electronic Engineers, 2023
Publish Date: 2023-08-30


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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Series: IEEE MTT-S International Microwave Symposium digest
ISSN: 0149-645X
ISSN-E: 2576-7216
ISSN-L: 0149-645X
ISBN: 979-8-3503-4764-7
ISBN Print: 979-8-3503-4765-4
Pages: 81 - 84
DOI: 10.1109/IMS37964.2023.10188119
Host publication: 2023 IEEE/MTT-S International Microwave Symposium - IMS 2023, 11-16 June 2023
Conference: International Microwave Symposium
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This work was supported in part by the Academy of Finland projects 6Genesis Flagship (grant number 346208) funding and Profi5 funding for Mathematics and AI: data insight for high-dimensional dynamics (HiDyn) (grant number 326291).
Academy of Finland Grant Number: 346208
Detailed Information: 346208 (Academy of Finland Funding decision)
326291 (Academy of Finland Funding decision)
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