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Automatic Modulation Recognition Based on the Optimized Linear Combination of Higher-Order Cumulants

Lookup NU author(s): Dr Husnain SheraziORCiD

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

Abstract

© 2022 by the authors.Automatic modulation recognition (AMR) is used in various domains—from general-purpose 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.

Publication metadata

Author(s): Hussain A, Alam S, Ghauri SA, Ali M, Sherazi HHR, Akhunzada A, Bibi I, Gani A

Publication type: Article

Publication status: Published

Journal: Sensors

Year: 2022

Volume: 22

Issue: 19

Print publication date: 01/10/2022

Online publication date: 02/10/2022

Acceptance date: 25/08/2022

Date deposited: 23/05/2024

ISSN (electronic): 1424-8220

Publisher: MDPI

URL: https://doi.org/10.3390/s22197488

DOI: 10.3390/s22197488

Data Access Statement: Not applicable.

PubMed id: 36236583

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