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Automatic modulation recognition based on the optimized linear combination of higher-order cumulants

Automatic modulation recognition (AMR) is used in various domains—from generalpurpose communication to many military applications—thanks to the growing popularity of the Internet of Things (IoT) and related communication technologies. In this research article, we propose an innovative idea of combin...

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Main Authors: Asad Hussain, Sheraz Alam, Sajjad A. Ghauri, Mubashir Ali, Husnain Raza Sherazi, Adnan Akhunzada, Iram Bibi, Abdullah Gani
Format: Article
Language:English
Published: MDPI 2022
Subjects:
QA75.5-76.95 Electronic computers. Computer science
TK7885-7895 Computer engineering. Computer hardware
Online Access:https://eprints.ums.edu.my/id/eprint/42508/1/FULL%20TEXT.pdf
https://eprints.ums.edu.my/id/eprint/42508/
https://doi.org/10.3390/s22197488
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spelling my.ums.eprints.425082024-12-31T03:22:36Z https://eprints.ums.edu.my/id/eprint/42508/ Automatic modulation recognition based on the optimized linear combination of higher-order cumulants Asad Hussain Sheraz Alam Sajjad A. Ghauri Mubashir Ali Husnain Raza Sherazi Adnan Akhunzada Iram Bibi Abdullah Gani QA75.5-76.95 Electronic computers. Computer science TK7885-7895 Computer engineering. Computer hardware Automatic modulation recognition (AMR) is used in various domains—from generalpurpose communication to many military applications—thanks to the growing popularity of the Internet of Things (IoT) and related communication technologies. In this research article, we propose an innovative idea of combining the classical mathematical technique of computing linear combinations (LCs) of cumulants with a genetic algorithm (GA) to create super-cumulants. These super-cumulants are further used to classify five digital modulation schemes on fading channels using the K-nearest neighbor (KNN). Our proposed classifier significantly improves the percentage recognition accuracy at lower SNRs when using smaller sample sizes. A comparison with existing techniques manifests the supremacy of our proposed classifier. MDPI 2022 Article NonPeerReviewed text en https://eprints.ums.edu.my/id/eprint/42508/1/FULL%20TEXT.pdf Asad Hussain and Sheraz Alam and Sajjad A. Ghauri and Mubashir Ali and Husnain Raza Sherazi and Adnan Akhunzada and Iram Bibi and Abdullah Gani (2022) Automatic modulation recognition based on the optimized linear combination of higher-order cumulants. Sensors, 22. pp. 1-16. https://doi.org/10.3390/s22197488
institution Universiti Malaysia Sabah
building UMS Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sabah
content_source UMS Institutional Repository
url_provider http://eprints.ums.edu.my/
language English
topic QA75.5-76.95 Electronic computers. Computer science
TK7885-7895 Computer engineering. Computer hardware
spellingShingle QA75.5-76.95 Electronic computers. Computer science
TK7885-7895 Computer engineering. Computer hardware
Asad Hussain
Sheraz Alam
Sajjad A. Ghauri
Mubashir Ali
Husnain Raza Sherazi
Adnan Akhunzada
Iram Bibi
Abdullah Gani
Automatic modulation recognition based on the optimized linear combination of higher-order cumulants
description Automatic modulation recognition (AMR) is used in various domains—from generalpurpose communication to many military applications—thanks to the growing popularity of the Internet of Things (IoT) and related communication technologies. In this research article, we propose an innovative idea of combining the classical mathematical technique of computing linear combinations (LCs) of cumulants with a genetic algorithm (GA) to create super-cumulants. These super-cumulants are further used to classify five digital modulation schemes on fading channels using the K-nearest neighbor (KNN). Our proposed classifier significantly improves the percentage recognition accuracy at lower SNRs when using smaller sample sizes. A comparison with existing techniques manifests the supremacy of our proposed classifier.
format Article
author Asad Hussain
Sheraz Alam
Sajjad A. Ghauri
Mubashir Ali
Husnain Raza Sherazi
Adnan Akhunzada
Iram Bibi
Abdullah Gani
author_facet Asad Hussain
Sheraz Alam
Sajjad A. Ghauri
Mubashir Ali
Husnain Raza Sherazi
Adnan Akhunzada
Iram Bibi
Abdullah Gani
author_sort Asad Hussain
title Automatic modulation recognition based on the optimized linear combination of higher-order cumulants
title_short Automatic modulation recognition based on the optimized linear combination of higher-order cumulants
title_full Automatic modulation recognition based on the optimized linear combination of higher-order cumulants
title_fullStr Automatic modulation recognition based on the optimized linear combination of higher-order cumulants
title_full_unstemmed Automatic modulation recognition based on the optimized linear combination of higher-order cumulants
title_sort automatic modulation recognition based on the optimized linear combination of higher-order cumulants
publisher MDPI
publishDate 2022
url https://eprints.ums.edu.my/id/eprint/42508/1/FULL%20TEXT.pdf
https://eprints.ums.edu.my/id/eprint/42508/
https://doi.org/10.3390/s22197488
_version_ 1821003227101921280
score 13.226497

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