Finnerty, Keith and Dooley, John and Farrell, Ronan (2014) Utilizing Sparse-Aware Volterra for Power Amplifier Behavioral Modeling. Proceedings from the 17th Research Colloquium on Communications and Radio Science into the 21st Century. ISSN 978-1-908996-33-6

Preview	Download (378kB) | Preview
Preview	Download (704kB) | Preview

Abstract

This paper presents a method for reducing the number of weights in a time series behavioral model for a power amplifier. The least-absolute shrinkage and selection operator (Lasso) algorithm is used to reduce the kernel size, preserving the important kernels, while eliminating the less important kernels. The algorithm is evaluated on a behavioral model for a class AB amplifier, the algorithm reduces the number of weights by greater than 70% without degrading model performance by a significant amount.

Item Type:	Article
Keywords:	Behavioral modeling; time series; Volterra; system identification; Lasso;
Academic Unit:	Faculty of Science and Engineering > Electronic Engineering
Item ID:	5363
Depositing User:	Ronan Farrell
Date Deposited:	04 Sep 2014 13:34
Journal or Publication Title:	Proceedings from the 17th Research Colloquium on Communications and Radio Science into the 21st Century
Publisher:	Royal Irish Academy
Refereed:	Yes
Funders:	Science Foundation Ireland under Grant No. 10/CE/I1853
URI:	Publisher
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

Repository Staff Only(login required)

Item control page

Downloads

Origin of downloads