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.
Derivation of Sensing Features for Maximum Cyclic Autocorrelation Selection Based Signal Detection
01.09.2019
138400 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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