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    CHANNEL ESTIMATION USING GAUSSIAN PROCESS REGRESSION

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    ITC_2019_19-13-04.pdf
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    Author
    Simeon, Richard
    Advisor
    Kim, Taejoon
    Perrins, Erik
    Affiliation
    Univ Kansas, Dept Electrical Engineering and Computer Science
    Issue Date
    2019-10
    
    Metadata
    Show full item record
    Publisher
    International Foundation for Telemetering
    Journal
    International Telemetering Conference Proceedings
    URI
    http://hdl.handle.net/10150/635284
    Additional Links
    http://www.telemetry.org/
    Abstract
    Gaussian process (GP) regression can be used in the interpolation of observed periodic channel estimates in OFDM transmission systems over both time and frequency in small-scale fading environments. Previous GP regression studies used the popular radial basis function as the GP kernel. In this study, we examine the performance of GP regression using a Bessel kernel with a semi-static hyperparameter vector. Results show that GP regression using the Bessel kernel outperforms the radial basis kernel, as well as traditional interpolation methods such as cubic spline and FIR interpolation, especially when training symbols are spaced far apart in time with respect to the channel coherence time.
    Type
    text
    Proceedings
    Language
    en_US
    ISSN
    0884-5123
    0074-9079
    Sponsors
    International Foundation for Telemetering
    Collections
    International Telemetering Conference Proceedings, Volume 55 (2019)

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