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

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submitted on 2023-12-25, 08:06 and posted on 2024-01-10, 11:28 authored by Asad Hussain, Sheraz Alam, Sajjad A. Ghauri, Mubashir Ali, Husnain Raza Sherazi, Adnan Akhunzada, Iram Bibi, Abdullah Gani

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.

Other Information

Published in: Sensors
License: https://creativecommons.org/licenses/by/4.0/
See article on publisher's website: https://dx.doi.org/10.3390/s22197488


The University of Doha for Science and Technology replaced the now-former College of the North Atlantic-Qatar after an Amiri decision in 2022. UDST has become and first national applied University in Qatar; it is also second national University in the country.

History

Language

  • English

Publisher

MDPI

Publication Year

  • 2022

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License.

Institution affiliated with

  • University of Doha for Science and Technology
  • College of Computing and Information Technology - UDST
  • College of the North Atlantic - Qatar (-2022)
  • School of Business and Information Technology - CNA-Q (-2022)