This paper presents a novel spectrum sensing technique based on selection diversity combining in cognitive radio networks. In general, a selection diversity combining scheme requires a period to select an optimal element, and spectrum sensing requires a period to detect a target signal. We consider that both these periods are required for the spectrum sensing based on selection diversity combining. However, conventional techniques do not consider both the periods. Furthermore, spending a large amount of time in selection and signal detection increases their accuracy. Because the required period for spectrum sensing based on selection diversity combining is the summation of both the periods, their lengths should be considered while developing selection diversity combining based spectrum sensing for a constant period. In reference to this, we discuss the spectrum sensing technique based on selection diversity combining. Numerical examples are shown to validate the effectiveness of the presented design techniques.

This paper provides theoretical analyses for maximum cyclic autocorrelation selection (MCAS)-based spectrum sensing techniques in cognitive radio networks. The MCAS-based spectrum sensing techniques are low computational complexity spectrum sensing in comparison with some cyclostationary detection. However, MCAS-based spectrum sensing characteristics have never been theoretically derived. 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 values match well with each other.