Fractional low order cyclostationary-based spectrum sensing in cognitive radio networks

Abstract

In this paper, we study the problem of cyclostationary spectrum sensing in cognitive radio networks based on cyclic properties of linear modulations. For this purpose, we use fractional order of observations in cyclic autocorrelation function (CAF).We derive the generalized likelihood ratio (GLR) for designing the detector. Therefore, the performance of this detector has been improved compared to previous detectors. We also find optimum value of the fractional order of observations in additive Gaussian noise. The exact performance of the GLR detector is derived analytically as well. The simulation results are presented to evaluate the performance of the proposed detector and compare its performance with their counterpart, so to illustrate the impact of the optimum value of fractional order over performance improvement of these detectors. Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2015.