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Data-Clustering-Assisted Digital Predistortion for 5G Millimeter-Wave Beamforming Transmitters With Multiple Dynamic Configurations
Author(s)
Date Issued
2021-03
Date Available
2021-03-10T17:25:12Z
Abstract
Motivated by data science, in this article, a data-clustering-assisted digital predistortion (DPD) is proposed to linearize millimeter-wave (mmWave) beamforming transmitters with multiple dynamic configurations. Based on the data analysis, similar transmitter states with different configurations can be clustered, resulting in a significant reduction of linearization states. Model complexity within the cluster can be further reduced by utilizing a penalty factor method. To validate the proposed concept, experiments were carried out on a 16-channel mmWave beamforming transmitter with configurable beam angle, operating frequency, and input power. Total 216 transmitter states can be reduced to 8, 20, and 40 states with unequal optimal model parameters for each state, without losing much performance. The proposed method can be extended to the scenario where a large scale of dynamic states can occur in complex transmitter structures in future wireless systems.
Sponsorship
Science Foundation Ireland
Other Sponsorship
National Natural Science Foundation of China
Type of Material
Journal Article
Publisher
IEEE
Journal
IEEE Transactions on Microwave Theory and Techniques
Volume
69
Issue
3
Start Page
1805
End Page
1816
Copyright (Published Version)
2020 IEEE
Language
English
Status of Item
Peer reviewed
ISSN
0018-9480
This item is made available under a Creative Commons License
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