User Detection Using Cyclostationary Feature Detection in Cognitive Radio Networks with Various Detection Criteria

Abstract

To identify the user’s presence by using non-cooperative detection methods in the Cognitive Radio (CR), networks used are Energy Detection (ED), Matched Filter Detection (MFD), and Cyclostationary Feature Detection (CFD). The signal power threshold is the critical parameter to identify whether the user is present or absent in the spectrum. In the literature, various authors proposed their research on spectrum sensing using CFD with static and predefined dynamic thresholds. In this paper, authors proposed the novel CFD with inverse covariance approach method for dynamic threshold estimated by using Generalized Likelihood Ratio Test (GLRT) and Neyman–Pearson (NP) observer detection criteria. The results show the performance of the proposed method as the probability of detection (PD) is increased and the probabilities of false alarm (Pfa) and missed detection (Pmd) have been reduced when compared with the existing methods. The results are simulated using Matlab software and the results are analyzed among the three parameters.