MURAL - Maynooth University Research Archive Library



    Early Stopping Criterion for Recursive Least Squares Training of Behavioural Models


    Loughman, Meabh and Barton, Sinead and Farrell, Ronan and Dooley, John (2021) Early Stopping Criterion for Recursive Least Squares Training of Behavioural Models. Wireless Personal Communications. ISSN 0929-6212

    [img]
    Preview
    Download (3MB) | Preview


    Share your research

    Twitter Facebook LinkedIn GooglePlus Email more...



    Add this article to your Mendeley library


    Abstract

    As the physical makeup of cellular base-stations evolve into systems with multiple parallel transmission paths the effort involved in modelling these complex systems increases considerably. One task in particular which contributes to signal distortion on each signal path, is the power amplifier. In power amplifier modelling, Recursive Least Squares has been used in the past to train Volterra models with memory terms, however instability can occur when training the model weights. This manuscript provides a computationally efficient technique to detect the onset of instability and subsequently to inform the decision when to stop adaptive training of dynamic nonlinear behavioural models and avoid the onset of instability. This technique is experimentally validated using four different signal modulation schemes.

    Item Type: Article
    Keywords: Behavioural modeling; power amplifier; recursive least squares; RLS; Volterra model;
    Academic Unit: Faculty of Science and Engineering > Electronic Engineering
    Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 14517
    Identification Number: https://doi.org/10.21203/rs.3.rs-452849/v1
    Depositing User: Ronan Farrell
    Date Deposited: 08 Jun 2021 16:41
    Journal or Publication Title: Wireless Personal Communications
    Publisher: Springer
    Refereed: Yes
    URI:

    Repository Staff Only(login required)

    View Item Item control page

    Downloads

    Downloads per month over past year