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

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.

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Abstract

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.

Item Type:	Article
Keyword:	Modulation recognition, K-nearest neighbor, Genetic algorithm, Higher-order cumulants
Subjects:	Q Science > QA Mathematics > QA1-939 Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK1-9971 Electrical engineering. Electronics. Nuclear engineering > TK7800-8360 Electronics > TK7885-7895 Computer engineering. Computer hardware
Department:	FACULTY > Faculty of Computing and Informatics
Depositing User:	SITI AZIZAH BINTI IDRIS -
Date Deposited:	31 Dec 2024 11:22
Last Modified:	31 Dec 2024 11:22
URI:	https://eprints.ums.edu.my/id/eprint/42508

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