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    A Low Complexity NARX Structure using Indirect Learning Architecture for Digital Pre-Distortion


    Varahram, Pooria, Dooley, John, Wang, Ziming, Finnerty, Keith and Farrell, Ronan (2017) A Low Complexity NARX Structure using Indirect Learning Architecture for Digital Pre-Distortion. In: RIA Research Colloquium: Communications and Radio Science for a Smarter World, March 2017, RIA, Dawson Street, Dublin. (Unpublished)

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    Abstract

    In this paper, we demonstrate a nonlinear autoregressive with exogenous input (NARX) DPD technique which is more compact, less computationally intensive and less susceptible to errors caused by noise in the PA output compared to an equivalent memory polynomial based DPD. Experimental validation is performed with a 20 MHz LTE signal for a GaN Doherty power amplifier.
    Item Type: Conference or Workshop Item (Paper)
    Keywords: power amplifier; nonlinear autoregressive; exogenous inputs; digital pre-distortion; indirect learning architecture;
    Academic Unit: Faculty of Science and Engineering > Electronic Engineering
    Item ID: 9030
    Depositing User: Ronan Farrell
    Date Deposited: 24 Nov 2017 11:03
    Refereed: No
    Related URLs:
    URI: https://mu.eprints-hosting.org/id/eprint/9030
    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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