Deep Learning Based Modulation Recognition

Leblebici M. M., Çalhan A., Cicioğlu M.

Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, cilt.1, sa.1, ss.1-20, 2023 (Hakemli Dergi)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 1 Sayı: 1
  • Basım Tarihi: 2023
  • Dergi Adı: Uludağ Üniversitesi Mühendislik Fakültesi Dergisi
  • Derginin Tarandığı İndeksler: TR DİZİN (ULAKBİM)
  • Sayfa Sayıları: ss.1-20
  • Bursa Uludağ Üniversitesi Adresli: Evet

Özet

The increasing signal diversity of communication technologies has revealed the need that these signals to be defined and classified. Fifth-generation (5G) and beyond wireless communication technologies have become indispensable communication tools for many applications. The automatic modulation recognition (AMR) technique has become a key component for many applications, especially the next-generation internet of things, smart cities, autonomous vehicles, and cognitive radio. In this study, a data set was created using eight different modulation types and modulation classification was made at different signal-to-noise ratios (SNR) using convolutional neural networks (CNN) from deep learning (DL) algorithms. As a result, while the SNR values were 10 dB, 20 dB, and 30 dB, CNN provided 80.76%, 99.89%, and 100% accuracy in the classification process, respectively.