Deep Learning Based Modulation Recognition
Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, cilt.28, sa.1, ss.123-140, 2023 (TRDizin)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 28 Sayı: 1
- Basım Tarihi: 2023
- Doi Numarası: 10.17482/uumfd.1161509
- Dergi Adı: Uludağ Üniversitesi Mühendislik Fakültesi Dergisi
- Derginin Tarandığı İndeksler: TR DİZİN (ULAKBİM)
- Sayfa Sayıları: ss.123-140
- 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.