Identify the user presence by GLRT and NP detection criteria in cognitive radio spectrum sensing. (16th August 2019)

• Record Type: Journal Article
• Authors: Budati, Anil Kumar; Valiveti, HimaBindu
• Other Names: Raj Jennifer S. guestEditor.; Chen Joy Iong‐Zong guestEditor.; Kotuliak Ivan guestEditor.; Kamel Khaled guestEditor.
• Is Part Of: International journal of communication systems. Volume 35:Number 2(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-08-16
• Subjects: CR -- GLRT -- MFD -- NP observer -- ROC; Telecommunication systems -- Periodicals; 621.382
• DOI: 10.1002/dac.4142
• Languages: English
• ISSNs: 1074-5351

Abstract

Summary: Spectrum sensing in cognitive radio networks is vital and is used for identifying the user presence or absence in the available spectrum. Energy detection and matched filter detection are the few methods to identify the user presence in the spectrum. There are various authors that proposed their research on spectrum sensing using matched filter detection with fixed threshold and predefined dynamic threshold. In this paper, authors proposed the novel matched filter detection method with dynamic threshold by using generalized likelihood ratio test (GLRT) and Neyman Pearson (NP) observer detection criteria. Due to which the probability of detection ( P D ) is increased, probability of false alarm ( P fa ) and probability of missed detection ( P md ) has been reduced when compare with the existing methods. The results are simulated using MATLab Software and also plotted the receiver operating characteristic (ROC) curve for estimation of the receiver sensitivity. Abstract : In this paper, authors proposed the novel matched filter detection method with dynamic threshold by using generalized likelihood ratio test (GLRT) and Neyman Pearson (NP) observer detection criteriaŠ. Due to which the probability of detection (PD) is increased, probability of false alarm ( P fa ) and probability of missed detection ( P md ) has been reduced when compare with the existing methods. The results are simulated using MATLab Software and also plotted the receiver operating characteristic (ROC) curve for estimation of the receiver sensitivity.