Maximum cyclic autocorrelation selection (MCAS)-based spectrum sensing is one of the low complexity spectrum sensing techniques in cyclostationary detection techniques. However, spectrum sensing features of MCAS- based spectrum sensing have never been theoretically derived. This paper provides a derivation result of spectrum sensing characteristics for MCAS-based spectrum sensing in cognitive radio networks. In this study, we derive closed form solutions for signal detection probability and false alarm probability for MCAS-based spectrum sensing. The theoretical values are compared with numerical examples, and the examples demonstrate that numerical and theoretical values match well with each other.


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    Titel :

    Derivation of Sensing Features for Maximum Cyclic Autocorrelation Selection Based Signal Detection


    Beteiligte:
    Narieda, Shusuke (Autor:in) / Cho, Daiki (Autor:in) / Ogasawara, Hiromichi (Autor:in) / Umebayashi, Kenta (Autor:in) / Fujii, Takeo (Autor:in) / Naruse, Hiroshi (Autor:in)


    Erscheinungsdatum :

    01.09.2019


    Format / Umfang :

    138400 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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