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    Utilizing Sparse-Aware Volterra for Power Amplifier Behavioral Modeling


    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

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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: Dr. 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:

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