AUTOMATIC RECOGNITION OF BOTH INTER AND INTRA CLASSES OF DIGITAL MODULATED SIGNALS USING ARTIFICIAL NEURAL NETWORK
In radio communication systems, signal modulation format recognition is a significant characteristic used in radio signal monitoring and identification. Over the past few decades, modulation formats have become increasingly complex, which has led to the problem of how to accurately and promptly reco...
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| Format: | Article |
| Language: | English |
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Taylor's University
2014-04-01
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| Series: | Journal of Engineering Science and Technology |
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| Online Access: | http://jestec.taylors.edu.my/Vol%209%20Issue%202%20April%2014/Volume%20(9)%20Issue%20(2)%20273-285.pdf |
| _version_ | 1819176245886189568 |
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| author | JIDE JULIUS POPOOLA |
| author_facet | JIDE JULIUS POPOOLA |
| author_sort | JIDE JULIUS POPOOLA |
| collection | DOAJ |
| description | In radio communication systems, signal modulation format recognition is a significant characteristic used in radio signal monitoring and identification. Over the past few decades, modulation formats have become increasingly complex, which has led to the problem of how to accurately and promptly recognize a modulation format. In addressing these challenges, the development of automatic modulation recognition systems that can classify a radio signal’s modulation format has received worldwide attention. Decision-theoretic methods and pattern recognition solutions are the two typical automatic modulation recognition approaches. While decision-theoretic approaches use
probabilistic or likelihood functions, pattern recognition uses feature-based methods. This study applies the pattern recognition approach based on statistical parameters, using an artificial neural network to classify five different digital modulation formats. The paper deals with automatic recognition of both inter-and intra-classes of digitally modulated signals in contrast to most of the existing algorithms in literature that deal with either inter-class or intra-class modulation format recognition. The results of this study show that accurate and prompt modulation recognition is possible beyond the lower bound of 5 dB commonly acclaimed in literature. The other significant contribution of this paper is the usage of the Python programming language which reduces
computational complexity that characterizes other automatic modulation recognition classifiers developed using the conventional MATLAB neural network toolbox. |
| first_indexed | 2024-12-22T21:07:42Z |
| format | Article |
| id | doaj.art-77db629eafce4cb4aa604ed7828cfbe6 |
| institution | Directory Open Access Journal |
| issn | 1823-4690 |
| language | English |
| last_indexed | 2024-12-22T21:07:42Z |
| publishDate | 2014-04-01 |
| publisher | Taylor's University |
| record_format | Article |
| series | Journal of Engineering Science and Technology |
| spelling | doaj.art-77db629eafce4cb4aa604ed7828cfbe62022-12-21T18:12:38ZengTaylor's UniversityJournal of Engineering Science and Technology1823-46902014-04-0192273285AUTOMATIC RECOGNITION OF BOTH INTER AND INTRA CLASSES OF DIGITAL MODULATED SIGNALS USING ARTIFICIAL NEURAL NETWORKJIDE JULIUS POPOOLA 0Department of Electrical and Electronic Engineering, Federal University of Technology, P.M.B. 704, Akure, Ondo State, Nigeria In radio communication systems, signal modulation format recognition is a significant characteristic used in radio signal monitoring and identification. Over the past few decades, modulation formats have become increasingly complex, which has led to the problem of how to accurately and promptly recognize a modulation format. In addressing these challenges, the development of automatic modulation recognition systems that can classify a radio signal’s modulation format has received worldwide attention. Decision-theoretic methods and pattern recognition solutions are the two typical automatic modulation recognition approaches. While decision-theoretic approaches use probabilistic or likelihood functions, pattern recognition uses feature-based methods. This study applies the pattern recognition approach based on statistical parameters, using an artificial neural network to classify five different digital modulation formats. The paper deals with automatic recognition of both inter-and intra-classes of digitally modulated signals in contrast to most of the existing algorithms in literature that deal with either inter-class or intra-class modulation format recognition. The results of this study show that accurate and prompt modulation recognition is possible beyond the lower bound of 5 dB commonly acclaimed in literature. The other significant contribution of this paper is the usage of the Python programming language which reduces computational complexity that characterizes other automatic modulation recognition classifiers developed using the conventional MATLAB neural network toolbox.http://jestec.taylors.edu.my/Vol%209%20Issue%202%20April%2014/Volume%20(9)%20Issue%20(2)%20273-285.pdfAutomatic modulation recognitionInter and intra modulation classesFeatures extraction keyArtificial neural network |
| spellingShingle | JIDE JULIUS POPOOLA AUTOMATIC RECOGNITION OF BOTH INTER AND INTRA CLASSES OF DIGITAL MODULATED SIGNALS USING ARTIFICIAL NEURAL NETWORK Journal of Engineering Science and Technology Automatic modulation recognition Inter and intra modulation classes Features extraction key Artificial neural network |
| title | AUTOMATIC RECOGNITION OF BOTH INTER AND INTRA CLASSES OF DIGITAL MODULATED SIGNALS USING ARTIFICIAL NEURAL NETWORK |
| title_full | AUTOMATIC RECOGNITION OF BOTH INTER AND INTRA CLASSES OF DIGITAL MODULATED SIGNALS USING ARTIFICIAL NEURAL NETWORK |
| title_fullStr | AUTOMATIC RECOGNITION OF BOTH INTER AND INTRA CLASSES OF DIGITAL MODULATED SIGNALS USING ARTIFICIAL NEURAL NETWORK |
| title_full_unstemmed | AUTOMATIC RECOGNITION OF BOTH INTER AND INTRA CLASSES OF DIGITAL MODULATED SIGNALS USING ARTIFICIAL NEURAL NETWORK |
| title_short | AUTOMATIC RECOGNITION OF BOTH INTER AND INTRA CLASSES OF DIGITAL MODULATED SIGNALS USING ARTIFICIAL NEURAL NETWORK |
| title_sort | automatic recognition of both inter and intra classes of digital modulated signals using artificial neural network |
| topic | Automatic modulation recognition Inter and intra modulation classes Features extraction key Artificial neural network |
| url | http://jestec.taylors.edu.my/Vol%209%20Issue%202%20April%2014/Volume%20(9)%20Issue%20(2)%20273-285.pdf |
| work_keys_str_mv | AT jidejuliuspopoola automaticrecognitionofbothinterandintraclassesofdigitalmodulatedsignalsusingartificialneuralnetwork |