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Hybrid Spectrum Sensing Algorithm for Cognitive Radio Network

by Syed Fahad Shirazi, Syed Hamad Shirazi, Syed Muslim Shah, Muhammad Khalil Shahid,
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 45 - Number 17
Year of Publication: 2012
Authors: Syed Fahad Shirazi, Syed Hamad Shirazi, Syed Muslim Shah, Muhammad Khalil Shahid,
10.5120/7003-9557

Syed Fahad Shirazi, Syed Hamad Shirazi, Syed Muslim Shah, Muhammad Khalil Shahid, . Hybrid Spectrum Sensing Algorithm for Cognitive Radio Network. International Journal of Computer Applications. 45, 17 ( May 2012), 25-30. DOI=10.5120/7003-9557

@article{ 10.5120/7003-9557,
author = { Syed Fahad Shirazi, Syed Hamad Shirazi, Syed Muslim Shah, Muhammad Khalil Shahid, },
title = { Hybrid Spectrum Sensing Algorithm for Cognitive Radio Network },
journal = { International Journal of Computer Applications },
issue_date = { May 2012 },
volume = { 45 },
number = { 17 },
month = { May },
year = { 2012 },
issn = { 0975-8887 },
pages = { 25-30 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume45/number17/7003-9557/ },
doi = { 10.5120/7003-9557 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:37:52.190624+05:30
%A Syed Fahad Shirazi
%A Syed Hamad Shirazi
%A Syed Muslim Shah
%A Muhammad Khalil Shahid,
%T Hybrid Spectrum Sensing Algorithm for Cognitive Radio Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 45
%N 17
%P 25-30
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Spectrum sensing plays a very provocative role in cognitive radio network. In order to utilize spectrum more efficiently and to exploit the primary user, spectrum sensing is accomplished. We proposed a new hybrid algorithm for detection of primary user in cognitive radio network. The theoretical analysis and simulation is also presented in this paper. This research work includes an analogy with Energy Based Detection and Cyclostationary Feature Detection. Our proposed algorithm is a flexible algorithm, the Cyclostationary feature algorithm act as feature extractor when primary user is present and function as detector when primary user is absent. The results show that it is optimum spectrum sensing algorithm under different SNR values. It has removed the shortcomings faced by both sensing algorithms i. e. Energy Based Detection and Cyclostationary Feature Detection.

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Index Terms

Computer Science
Information Sciences

Keywords

Power spectral density cyclic correlation function mean square spectrum hybrid spectrum sensing

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