In future wireless systems, efficient spectrum usage enabled by technologies such as cognitive radio (CR), dynamic spectrum access, non-orthogonal multiuser within licensed and unlicensed band requires knowledge of prevalence situation in a frequency band through learning. It is critical for wireless devices to identify the type and number of users and their waveforms in a frequency band at a given time. In this paper, this problem is formulated as a multi-class classification problem for accurate spectrum situation prediction under complicated coexistence scenarios. Deep learning is chosen for obtaining the statistics of the different coexisting wireless systems through learning from superimposed radio frequency (RF) data. Specifically, Deep Neural Network (DNN), Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) are adopted and designed. They are trained using real RF traces for different coexisting scenarios collected from a USRP based testbed to identify the presence of signals with varying levels of Signal to Noise Ratio, even when the signal is superimposed with other signals as sources of interference. Experimental results demonstrate the potential of deep learning for spectrum situation prediction under complicated coexistence scenarios.


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    Titel :

    Spectrum Occupancy Prediction in Coexisting Wireless Systems Using Deep Learning


    Beteiligte:
    Omotere, Oluwaseyi (Autor:in) / Fuller, John (Autor:in) / Qian, Lijun (Autor:in) / Han, Zhu (Autor:in)


    Erscheinungsdatum :

    01.08.2018


    Format / Umfang :

    1323216 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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