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

Abstract

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.