Performance analysis of HFDI computing algorithm in intelligent networks

ABSTRACT

Cognitive 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 (P _D), and probability of false alarm (P _fa). Apparently, there is less focus on which samples of P _fa are majorly affected. A method of Hybrid Filter Detection with Inverse covariance matrix (HFDI) is proposed to identify the P _fa affected samples and to minimize the P _fa with maximize of P _D. The proposed HFDI method uses generalized log likelihood ratio test (GLRT) to measure the P _fa and P _D. The measured P _fa and P _D are compared with the NP observer approach, HMF, and Divya Joshi-proposed methods. Additionally, a parameter probability of missed detection (P _md) is measured between the proposed HFDI and NP observer approach. The proposed HFDI method outperforms Divya Joshi-proposed method by 13% in terms of P _D, and the P _fa is also improved by 7.5%. ROC is a parameter used to analyze the performance between P _D and P _fa and is compared with the existing compressed sensing method.

KEYWORDS:

• Cognitive radio
• energy detector
• matched filter
• cyclostationary feature detector
• hybrid matched filter