On-Demand Real-Time Optimizable Dynamic Model Sizing for Digital Predistortion of Broadband RF Power Amplifiers

Files in This Item:
File Description SizeFormat 
OnDemandDPD.pdf2.45 MBAdobe PDFDownload
Title: On-Demand Real-Time Optimizable Dynamic Model Sizing for Digital Predistortion of Broadband RF Power Amplifiers
Authors: Li, YueZhu, Anding
Permanent link: http://hdl.handle.net/10197/12020
Date: Jul-2020
Online since: 2021-03-10T16:47:12Z
Abstract: © 1963-2012 IEEE. In this article, we present a dynamic model sizing approach for digital predistortion (DPD) of broadband radio-frequency power amplifiers. By employing a novel model structure adaptation algorithm, the DPD model structure can be adaptively adjusted during its real-time deployment to keep the optimum size and complexity under different operation conditions. Power consumption of DPD can be reduced by on-demand automatic model structure adaptation instead of reusing the same model structure for all power levels and band allocations. To realize dynamic model sizing, the adaptation algorithm explores new potential terms based on prior knowledge of the model structure and prunes the DPD model with a stepwise backward regression method. Experimental results show that the algorithm can quickly find the optimum model structure when the operation condition changes. During the adaptation, it can also maintain robust linearization performance with a relatively low computational complexity and thus demonstrates itself as a suitable solution to the linearization of future broadband wireless systems.
Funding Details: Science Foundation Ireland
Type of material: Journal Article
Publisher: IEEE
Journal: IEEE Transactions on Microwave Theory and Techniques
Volume: 68
Issue: 7
Start page: 2891
End page: 2901
Copyright (published version): 2020 IEEE
Keywords: Adaptation modelsHeuristic algorithmsPower demandComputational modelingReal-time systemsComplexity theoryMatching pursuit algorithmsBehavioral modelingDigital predistortion (DPD)LinearizationPower amplifiers (PAs)PruningVolterra series
DOI: 10.1109/TMTT.2020.2982165
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

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 research.repository@ucd.ie and the item will be withdrawn immediately. The author or person responsible for depositing the article will be contacted within one business day.