Akademik Veri Yönetim Sistemi
International Informatics Congress (IIC2022) , Batman, Türkiye, 17 - 19 Şubat 2022, ss.123-129
The idea of optimizing communication systems using deep learning techniques is promising
for the future. This new learning method is expected to reduce the need for high power consuming
electronic circuits for solving complex mathematical expressions in communication systems. The use of
deep learning techniques for modulation sensor designs, which is one of the points in need of
improvement in communication systems, will contribute to the literature. In this study, it is aimed to
design a CNN-based modulation detector that detects AM-DSB modulated signals among 26 different
modulation techniques, each of which has equal SNR values, by using the HisarMod2019 data set.
MobilNet architecture was used for experimental studies. With this architecture, 100% F1-score value
was obtained. This result shows that the used architecture has the ability to recognize AM-DSB samples
in the correlation between classification ability.