MURAL - Maynooth University Research Archive Library



    Acceleration of Digital Pre- Distortion Training Using Selective Partitioning


    Loughman, Meabh and Byrne, Declan and Farrell, Ronan and Dooley, John (2022) Acceleration of Digital Pre- Distortion Training Using Selective Partitioning. In: 2022 IEEE Topical Conference on RF/Microwave Power Amplifiers for Radio and Wireless Applications (PAWR), 16 - 19 January 2022, Las Vegas, Nevada, USA.

    [img]
    Preview
    Download (410kB) | Preview
    Official URL: https://doi.org/10.1109/PAWR53092.2022.9719839


    Share your research

    Twitter Facebook LinkedIn GooglePlus Email more...



    Add this article to your Mendeley library


    Abstract

    In recent years model and Digital Pre-Distortion dimension reduction has been widely researched. The oper- ations involved when running DPD are often far less than those needed during the training of the DPD coefficients. The proposed partitioned Least Squares (LS) adaptation allows a selected subset of DPD coefficients to be updated while the remaining coefficients are held constant. This technique allows a more adaptive training procedure, improved interpretability of the important DPD coefficient’s during training and the ability to partition the DPD function into specific groups. The Frisch-Waugh-Lovell (FWL) theorem is exploited to partition the coefficients of a DPD basis function trained using LS regression. The proposed methodology was experimentally validated with a Generalized Memory Polynomial (GMP) DPD function, used to linearize a 5W power amplifier (PA) driven by a 40MHz 5G-NR signal.

    Item Type: Conference or Workshop Item (Paper)
    Keywords: Acceleration; Digital Pre-Distortion Training; Selective Partitioning;
    Academic Unit: Faculty of Science and Engineering > Electronic Engineering
    Item ID: 15642
    Identification Number: https://doi.org/10.1109/PAWR53092.2022.9719839
    Depositing User: Ronan Farrell
    Date Deposited: 08 Mar 2022 15:43
    Refereed: Yes
    URI:

      Repository Staff Only(login required)

      View Item Item control page

      Downloads

      Downloads per month over past year