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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) |
| Persistent link: | http://urn.fi/urn:nbn:fi-fe20230830113360 |
| Language: | English |
| Published: |
Institute of Electrical and Electronic Engineers,
2023
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| Publish Date: | 2023-08-30 |
| Description: |
AbstractThis 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. see all
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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 |
| OADOI: | https://oadoi.org/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 |
| Subjects: | |
| 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 326291 |
| Detailed Information: |
346208 (Academy of Finland Funding decision) 326291 (Academy of Finland Funding decision) |
| Copyright information: |
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