Performance analysis of HFDI computing algorithm in intelligent networks

Abstract

ABSTRACTCognitive radio (CR) system plays a major role in spectrum sensing. In cognitive radio, the spectrum slot is assigned to user intellectually according to a prescribed algorithm. Different researchers have proposed different spectrum sensing methods to plot the receiver operating characteristics (ROCs), probability of detection (PD), and probability of false alarm (Pfa). Apparently, there is less focus on which samples of Pfa are majorly affected. A method of Hybrid Filter Detection with Inverse covariance matrix (HFDI) is proposed to identify the Pfa affected samples and to minimize the Pfa with maximize of PD. The proposed HFDI method uses generalized log likelihood ratio test (GLRT) to measure the Pfa and PD. The measured Pfa and PD are compared with the NP observer approach, HMF, and Divya Joshi-proposed methods. Additionally, a parameter probability of missed detection (Pmd) is measured between the proposed HFDI and NP observer approach. The proposed HFDI method outperforms Divya Joshi-proposed method by 13% in terms of PD, and the Pfa is also improved by 7.5%. ROC is a parameter used to analyze the performance between PD and Pfa and is compared with the existing compressed sensing method.