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Finnerty, Keith, 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
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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 |
| Related URLs: | |
| 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 |
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