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

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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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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Atilim University, Ankara, Turkey

    Remziye Büsra Coruk & Bengisu Yalcinkaya Gokdogan

  2. Department of Information and Communication Technologies, University of Pompeu Fabra, Barcelona, Spain

    Mohamed Benzaghta

  3. Department of Electrical and Electronics Engineering, Gazi University, Ankara, Turkey

    Ali Kara

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  1. Remziye Büsra Coruk
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  2. Bengisu Yalcinkaya Gokdogan
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  3. Mohamed Benzaghta
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Correspondence to Remziye Büsra Coruk.

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Coruk, R.B., Gokdogan, B.Y., Benzaghta, M. et al. On the Classification of Modulation Schemes Using Higher Order Statistics and Support Vector Machines. Wireless Pers Commun 126, 1363–1381 (2022). https://doi.org/10.1007/s11277-022-09795-8

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  • DOI: https://doi.org/10.1007/s11277-022-09795-8

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