Modulation Classification in a Multipath Fading Channel Using Deep Learning: 16QAM, 32QAM and 64QAM
Abdullah Samarkandi, Alhussain Almarhabi, Hatim Alhazmi, Mofadal Alymani, Mohsen H. Alhazmi, Yudong Yao · 2021
A method based on a constellation diagram is proposed to identify QAM modulation of different orders in static, slow, and frequency selective fading channels. Although constellation diagrams have been studied and classified in literature, most of the work focused on noise. Little has been done to study the effect of multipath fading channels. We develop a highly accurate modulation classification method by exploiting deep learning with the constellation diagram. Based on the experimental results, our CNN model achieves a classification accuracy of 100% at −10 dB signal-to-noise ratio (SNR) under a multipath Rayleigh fading channel.