Pareto-Optimal Multimetric Model Extraction for Digital Predistortion of RF Power Amplifiers for Error Spectrum Redistribution

In complex spectrum coordination scenarios in wireless communications, different frequency regions may have distinct linearity requirements. Digital predistortion (DPD), a widely used linearization technique, must address these varying needs properly by effectively redistributing the linearity error...

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Bibliographic Details
Published in:IEEE transactions on microwave theory and techniques Vol. 73; no. 4; pp. 2230 - 2241
Main Authors: Yin, Hang, Zhu, Anding
Format: Journal Article
Language:English
Published: New York IEEE 01.04.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adaptation models
Computational modeling
Constrained optimization
digital predistortion (DPD)
Identification methods
Kernel functions
Linearity
Linearization
Mathematical models
Measurement uncertainty
multiobjective optimization
Nonlinear distortion
Optimization
Pareto optimum
power amplifier (PA)
Power amplifiers
Predistortion
quadratically constrained quadratic programming (QCQP)
Time-frequency analysis
Wireless communication
Wireless communications
ISSN:0018-9480, 1557-9670
Online Access:Get full text
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Summary:In complex spectrum coordination scenarios in wireless communications, different frequency regions may have distinct linearity requirements. Digital predistortion (DPD), a widely used linearization technique, must address these varying needs properly by effectively redistributing the linearity errors across different frequency regions. To cope with this issue, this article proposes a model extraction method to identify Pareto-optimal coefficients for DPD to meet multimetric requirements, in which distinct requirements on multiple linearity metrics across different frequency regions are posed. Extensive experimental results on a Doherty power amplifier (PA) demonstrate that the proposed method can effectively find the desired solutions during DPD coefficient extraction for a given model, accommodating various spectral error distributions. This allows for enhanced linearization performance in designated frequency regions without damaging performance in other regions. In addition, the proposed method can satisfy certain multimetric requirements with significantly fewer model kernel functions, potentially reducing the digital processing power of DPD.
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ISSN:0018-9480
1557-9670
DOI:10.1109/TMTT.2024.3467250