Decomposed Vector Rotation-Based Behavioral Modeling for Digital Predistortion of RF Power Amplifiers

Files in This Item:
File Description SizeFormat 
TMTT-2014-09-0998_Final.pdf705.67 kBAdobe PDFDownload
Title: Decomposed Vector Rotation-Based Behavioral Modeling for Digital Predistortion of RF Power Amplifiers
Authors: Zhu, Anding
Permanent link: http://hdl.handle.net/10197/8424
Date: 14-Jan-2015
Abstract: A new behavioral model for digital predistortion of radio frequency (RF) power amplifiers (PAs) is proposed in this paper. It is derived from a modified form of the canonical piecewise-linear (CPWL) functions using a decomposed vector rotation (DVR) technique. In this model, the nonlinear basis function is constructed from piecewise vector decomposition, which is completely different from that used in the conventional Volterra series. Theoretical analysis has shown that this model is much more flexible in modeling RF PAs with non-Volterra-like behavior, and experimental results confirmed that the new model can produce excellent performance with a relatively small number of coefficients when compared to conventional models.
Funding Details: Science Foundation Ireland
Type of material: Journal Article
Publisher: IEEE
Copyright (published version): 2015 IEEE
Keywords: Behavioral modeling;Canonical piecewiselinear;Digital predistortion;Power amplifiers;Radio frequency;Volterra series;Wireless
DOI: 10.1109/TMTT.2014.2387853
Language: en
Status of Item: Peer reviewed
Appears in Collections:Electrical and Electronic Engineering Research Collection

Show full item record

SCOPUSTM   
Citations 10

26
Last Week
0
Last month
checked on Jun 23, 2018

Download(s) 50

16
checked on May 25, 2018

Google ScholarTM

Check

Altmetric


This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. For other possible restrictions on use please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.