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Wireless Personal Communications
Published in: Wireless Personal Communications 2/2022

11-06-2022

On the Classification of Modulation Schemes Using Higher Order Statistics and Support Vector Machines

Authors: Remziye Büsra Coruk, Bengisu Yalcinkaya Gokdogan, Mohamed Benzaghta, Ali Kara

Published in: Wireless Personal Communications | Issue 2/2022

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Abstract

The recognition of modulation schemes in military and civilian applications is a major task for intelligent receiving systems. Various Automatic Modulation Classification (AMC) algorithms have been developed for this purpose in the literature. However, classification with low computational complexity as well as reasonable processing time is still a challenge. In this paper, a feature-based approach along with various classifiers is employed based on statistical features as well as higher-order moments and cumulants. An over-the-air (OTA) recorded dataset consisting of four analog and ten digital modulation schemes are used for testing the proposed method at 0–20 dB SNR. The overall accuracy for quadratic Support Vector Machine (SVM) is found to be as high as 98% at 10 dB. The comparison of the results with other AMC papers published in the literature indicates that the proposed method present higher accuracy, especially for realistic channel induced OTA dataset.
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Metadata
Title
On the Classification of Modulation Schemes Using Higher Order Statistics and Support Vector Machines
Authors
Remziye Büsra Coruk
Bengisu Yalcinkaya Gokdogan
Mohamed Benzaghta
Ali Kara
Publication date
11-06-2022
Publisher
Springer US
Published in
Wireless Personal Communications / Issue 2/2022
Print ISSN: 0929-6212
Electronic ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-022-09795-8

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