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Signal Type Detection in CRN :A Machine Learning Framework Using Spectral Correlation Feature

by Nayan Basumatary, Bhabesh Nath, Nityananda Sarma
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 177 - Number 7
Year of Publication: 2017
Authors: Nayan Basumatary, Bhabesh Nath, Nityananda Sarma
10.5120/ijca2017915792

Nayan Basumatary, Bhabesh Nath, Nityananda Sarma . Signal Type Detection in CRN :A Machine Learning Framework Using Spectral Correlation Feature. International Journal of Computer Applications. 177, 7 ( Nov 2017), 1-7. DOI=10.5120/ijca2017915792

@article{ 10.5120/ijca2017915792,
author = { Nayan Basumatary, Bhabesh Nath, Nityananda Sarma },
title = { Signal Type Detection in CRN :A Machine Learning Framework Using Spectral Correlation Feature },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2017 },
volume = { 177 },
number = { 7 },
month = { Nov },
year = { 2017 },
issn = { 0975-8887 },
pages = { 1-7 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume177/number7/28682-2017915792/ },
doi = { 10.5120/ijca2017915792 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:45:11.864776+05:30
%A Nayan Basumatary
%A Bhabesh Nath
%A Nityananda Sarma
%T Signal Type Detection in CRN :A Machine Learning Framework Using Spectral Correlation Feature
%J International Journal of Computer Applications
%@ 0975-8887
%V 177
%N 7
%P 1-7
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Spectrum sensing is the core component in cognitive radio for ensuring effective dynamic spectrum access.Accurate signal classification in fading channels and low signal to noise ratio environment is a major challenge.Due to lack of information about the modulation scheme used it cannot distinguish whether signal present is that of the primary or any other secondary user communication.In this paper a multiclass modulation classification hierarchical framework is proposed which exploits the cyclostationary features extracted for identifying modulation form without any priori knowledge of signal properties like frequency,phase and symbol rate.The cyclostationary features extracted using spectral correlation analysis at each secondary user are treated as features and fed into the framework.The classification done is based on one-against-all approach to get the modulation scheme of the signal under observation. The performance of proposed framework for each classifiers used is quantified in terms of detection accuracy,average training time and classification delay. It is demonstrated through simulation that an optimal feature set can be obtained to classify a range of modulation schemes with the proposed hierarchical framework. The proposed framework is found to be effective for modulation detection of signals when compared with two existing methods.

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Index Terms

Computer Science
Information Sciences

Keywords

Modulation recognition Cyclostationary Features Multi Class Classification

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