Data-Clustering-Assisted Digital Predistortion for 5G Millimeter-Wave Beamforming Transmitters With Multiple Dynamic Configurations

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Title: Data-Clustering-Assisted Digital Predistortion for 5G Millimeter-Wave Beamforming Transmitters With Multiple Dynamic Configurations
Authors: Yin, HangYu, ZhiqiangYu, ChaoZhu, Andinget al.
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Date: Mar-2021
Online since: 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.
Funding Details: Science Foundation Ireland
Funding Details: 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
Keywords: BeamformingClusteringDigital predistortionK-meansMillimeter waveMultiple input multiple outputPower amplifier
DOI: 10.1109/tmtt.2020.3039747
Language: en
Status of Item: Peer reviewed
ISSN: 0018-9480
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Appears in Collections:Electrical and Electronic Engineering Research Collection

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