Complexity-Reduced Model Adaptation for Digital Predistortion of RF Power Amplifiers With Pretraining-Based Feature Extraction

Files in This Item:
File Description SizeFormat 
Pretraining DPD.pdf4.48 MBAdobe PDFDownload
Title: Complexity-Reduced Model Adaptation for Digital Predistortion of RF Power Amplifiers With Pretraining-Based Feature Extraction
Authors: Li, YueWang, XiaoyuZhu, Anding
Permanent link:
Date: Mar-2021
Online since: 2021-03-10T17:29:02Z
Abstract: In this article, we present a new method to reduce the model adaptation complexity for digital predistortion (DPD) of radio frequency (RF) power amplifiers (PAs) under varying operating conditions, using pretrained transformation of model coefficients. Experimental studies show that the PA behavior variations can be effectively tracked using a small number of ``transformed'' coefficients, even with large deviations in its output characteristics. Based on this discovery, to avoid reextracting all the original coefficients every time when the operating condition changes, we propose to conduct a one-time off-line pretraining stage to extract the common features of PA behaviors under different operating conditions first. The online model adaptation process will then only need to identify a small number of transformed coefficients, which can result in a drastic reduction in the computational complexity of the model adaptation process. The proposed solution is validated by experimental results considering varying signal bandwidth and output power levels on a high-efficiency gallium-nitride Doherty PA, where the computational complexity is significantly reduced and the system performance is not compromised.
Funding Details: Science Foundation Ireland
Type of material: Journal Article
Publisher: IEEE
Journal: IEEE Transactions on Microwave Theory and Techniques
Volume: 69
Issue: 3
Start page: 1780
End page: 1790
Copyright (published version): 2020 IEEE
Keywords: Adaption modelsFeature extractionComputational modelingPrincipal component analysisComputational complexityRadio frequencyData models
DOI: 10.1109/tmtt.2020.3039788
Language: en
Status of Item: Peer reviewed
ISSN: 0018-9480
This item is made available under a Creative Commons License:
Appears in Collections:Electrical and Electronic Engineering Research Collection

Show full item record

Page view(s)

Last Week
Last month
checked on Apr 11, 2021


checked on Apr 11, 2021

Google ScholarTM



If you are a publisher or author and have copyright concerns for any item, please email and the item will be withdrawn immediately. The author or person responsible for depositing the article will be contacted within one business day.