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

Coruk R. B., Gokdogan B. Y., Benzaghta M., KARA A.

WIRELESS PERSONAL COMMUNICATIONS, cilt.126, sa.2, ss.1363-1381, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 126 Sayı: 2
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1007/s11277-022-09795-8
  • Dergi Adı: WIRELESS PERSONAL COMMUNICATIONS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, PASCAL, Applied Science & Technology Source, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC
  • Sayfa Sayıları: ss.1363-1381
  • Anahtar Kelimeler: Modulation classification, Feature extraction, Support vector machines, Analog modulation, Digital modulation, RECOGNITION, DECOMPOSITION, ALGORITHM, CUMULANTS
  • Gazi Üniversitesi Adresli: Evet

Özet

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.