The LoRa modulation scheme is becoming one of the most adopted Internet of Things wireless physical layer due to its ability to transmit data over long distances with low power requirements. Typical demodulation techniques for LoRa utilize variants of non-coherent Frequency Shift Keying demodulation. This paper aims to capitalize on the robustness of deep learning techniques, specifically by using convolutional neural networks to demodulate LoRa symbols. We achieve this by building a dataset consisting of emulated time-domain LoRa symbols across a range of channel impairments; namely, we examine additive white Gaussian noise together with carrier frequency offset and time offset. The presented results show an improvement when utilizing deep learning over typical non-coherent detection while performing very close to the optimal matched filter.


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    Titel :

    LoRa Signal Demodulation Using Deep Learning, a Time-Domain Approach


    Beteiligte:
    Dakic, Kosta (Autor:in) / Al Homssi, Bassel (Autor:in) / Al-Hourani, Akram (Autor:in) / Lech, Margaret (Autor:in)


    Erscheinungsdatum :

    01.04.2021


    Format / Umfang :

    2705532 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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