Statistical Spectrum Sensing in Cognitive Radio

Author

Azim, Ali Waqar ; Khalid, S.S. ; Abrar, Shafayat

Author_Institution

Grenoble Inst. of Technol., ENSIMAG, Grenoble, France

fYear

2012

fDate

17-19 Dec. 2012

Firstpage

145

Lastpage

152

Abstract

Statistical spectrum sensing is a promising method which can reliably detect the primary users while requiring little prior information in cognitive radio networks. In this paper, we present an overview of sensing methods based on Goodness-of-Fit tests. We discuss the performance of Energy Detector (ED) sensing, Anderson Darling (AD) sensing, Cram´er VonMises(CVM) sensing and Order Statistic (OS) sensing and we compare the results using Monte-Carlo simulations. It is shown that OS sensing outperforms ED sensing, CVM sensing and AD sensing. Next it is shown through simulations that the OS test statistic does not provide maximum probability of detection for a desired probability of false alarm and results are provided showing the regions of high probability of detection for desired probability of false alarm.

Keywords

Monte Carlo methods; cognitive radio; probability; radio networks; sensors; AD sensing; Anderson Darling sensing; CVM sensing; Cram´er VonMises sensing; ED sensing; Monte-Carlo simulation; OS sensing; cognitive radio network; energy detector sensing; false alarm probability; goodness-of-fit testing; maximum probability detection; order statistic sensing; primary user detection; statistical spectrum sensing; Information technology;

fLanguage

English

Publisher

ieee

Conference_Titel

Frontiers of Information Technology (FIT), 2012 10th International Conference on

Conference_Location

Islamabad

Print_ISBN

978-1-4673-4946-8

Type

conf

DOI

10.1109/FIT.2012.34

Filename

6424313