Optimized Low-Complexity Implementation of Least Squares-Based Model Extraction for Digital Predistortion of RF Power Amplifiers

Files in This Item:
File Description SizeFormat 
TMTT201203_LeiGuan_Final_Draft.pdf1.43 MBAdobe PDFDownload
Title: Optimized Low-Complexity Implementation of Least Squares-Based Model Extraction for Digital Predistortion of RF Power Amplifiers
Authors: Guan, Lei
Zhu, Anding
Permanent link: http://hdl.handle.net/10197/8646
Date: Mar-2012
Abstract: Least squares (LS) estimation is widely used in model extraction of digital predistortion for RF power amplifiers. In order to reduce computational complexity and implementation cost, it is desirable to use a small number of training samples in the model parameter estimation. However, due to strong correlations between data samples in a real transmit signal, the ill-conditioning problem becomes severe in standard LS, which often leads to large errors occurring in model extraction. Using a short training sequence can also cause mismatch between the statistical properties of the training data and the actual signal that the amplifier transmits, which could degrade the linearization performance of the digital predistorter. In this paper, we propose first to use a 1-bit ridge regression algorithm to eliminate the ill-conditioning problem in the LS estimation and then use root-mean-squares based coefficients weighting and averaging approach to reduce the errors caused by the statistical mismatch. Experimental results show that the proposed approach can produce excellent model extraction accuracy with only a very small number of training samples, which dramatically reduces the computational complexity and the system implementation cost.
Type of material: Journal Article
Publisher: IEEE
Copyright (published version): 2012 IEEE
Keywords: Digital predistortion;Least squares;Model extraction;Power amplifier
DOI: 10.1109/TMTT.2011.2182656
Language: en
Status of Item: Peer reviewed
Appears in Collections:Electrical and Electronic Engineering Research Collection

Show full item record

SCOPUSTM   
Citations 5

50
Last Week
1
Last month
checked on Jun 23, 2018

Download(s) 50

24
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.