Multi-Channel Learning with Preprocessing for Automatic Modulation Order Separation

Sumen G., Celebi B. A., Kurt G. K., Gorcin A., Basaran S. T.

27th IEEE Symposium on Computers and Communications, ISCC 2022, Rhodes, Yunanistan, 30 Haziran - 03 Temmuz 2022, cilt.2022-June identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası: 2022-June
  • Doi Numarası: 10.1109/iscc55528.2022.9912830
  • Basıldığı Şehir: Rhodes
  • Basıldığı Ülke: Yunanistan
  • Anahtar Kelimeler: Automatic modulation classification, convolutional neural network, cumulant, deep learning, feature extraction
  • İstanbul Teknik Üniversitesi Adresli: Evet

Özet

© 2022 IEEE.Automatic modulation classification (AMC) with deep learning (DL) based methods has been studied in recent years and improvements have been shown in many studies; however, it has been difficult to design a classifier that can distinguish modulation orders such as 16-QAM and 64-QAM, with high accuracy. In this study, the distinction performance of 16-QAM and 64-QAM modulation orders increased by feeding the features obtained during the preprocessing stage to the multi-channel convolutional long short-term deep neural network (MCLDNN). Simulation results indicate performance improvements, particularly at the low SNR region. Furthermore, the proposed method can be extended for the separation of other orders of QAM and other digital modulations.