Mixture of Experts Approach for Piecewise Modeling and Linearization of RF Power Amplifiers

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Title: Mixture of Experts Approach for Piecewise Modeling and Linearization of RF Power Amplifiers
Authors: Brihuega, AlbertoAbdelaziz, MahmoudAnttila, LauriLi, YueZhu, AndingValkama, Mikko
Permanent link: http://hdl.handle.net/10197/12388
Date: Jan-2022
Online since: 2021-08-09T11:54:13Z
Abstract: Piecewise behavioral models are commonly adopted for modeling and linearization of RF power amplifiers (PAs) that exhibit strong amplitude-dependent nonlinear distortion characteristics, as global polynomial approximations tend to underperform in such scenarios. In this article, we consider a new piecewise model for PAs based on the mixture of experts (ME) approach, which builds on a probabilistic model that allows the different submodels to cooperate--as opposed to operating in an independent fashion that is commonly the case in existing reference methods. We first introduce the ME framework theory while also extend it such that it can be applied to model complex baseband signals and nonlinearities. Then, we show how the ME model allows overcoming some of the intrinsic shortcomings that existing piecewise behavioral models commonly exhibit, which translates into improved modeling accuracy and improved linearization performance. Furthermore, the extension of the ME approach to a tree-structured regression model, referred to as the hierarchical ME model, is also introduced and shown to provide further performance improvements over the basic ME approach. The proposed solutions are validated with extensive RF measurements, covering both PA direct modeling and digital predistortion (DPD)-based linearization, on a gallium nitride (GaN) load-modulated balanced PA, on a GaN Doherty PA, and on a class AB GaN high electron mobility transistor PA, while being compared against several state-of-the-art piecewise methods. The results demonstrate that the ME framework-based models outperform the state of the art.
Funding Details: Academy of Finland
Nokia Bell Labs
Tampere University
Type of material: Journal Article
Publisher: IEEE
Journal: IEEE Transactions on Microwave Theory and Techniques
Volume: 70
Issue: 1
Start page: 380
End page: 391
Copyright (published version): 2021 IEEE
Keywords: Behavioral modelingDigital predistortion (DPD)5G New Radio (NR)Mixture of experts (ME)Nonlinear distortionPiecewise modelsPower amplifiers (PAs)
DOI: 10.1109/tmtt.2021.3098867
Language: en
Status of Item: Peer reviewed
ISSN: 0018-9480
This item is made available under a Creative Commons License: https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
Appears in Collections:Electrical and Electronic Engineering Research Collection

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