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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    Title :

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


    Contributors:


    Publication date :

    2019-09-01


    Size :

    138400 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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