| Efficient CNN-Based Automatic Modulation Classification in UWA Communication Systems Using Constellation Diagrams and Gabor Filtering |
| Paper ID : 1080-ICEEM2023 (R1) |
| Authors: |
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Mohamed Ahmed Abd El-Moneim *1, El-Sayed Mahmoud EL-Rabaie2, Fathi Sayed2, Khaled Ramadan3, Nariman Abdel-Salam4, Khalil Ramadan5 1Department of Telecommunication, Faculty of Engineering, Egyptian Russian University, Cairo, Egypt 2Electronics and Electrical Communications Engineering Department Faculty of Electronic Engineering, Menoufia University Menouf 32952, Egypt 3Communication and Computer Engineering Department The Higher Institute of Engineering at Al-Shorouk City Cairo, Egypt 4Communications and Electronics Engineering Department Faculty of Engineering, Canadian International College (CIC) Giza, Egypt 5dept. of Electronics and Electrical Communications Faculty of Electronic Engineering, Menoufia University |
| Abstract: |
| Underwater Acoustic (UWA) communication channels are varying in nature due to various underwater conditions. The efficiency of the UWA communication system can be increased by changing transmission parameters over UWA channels based on the state of the channel. The aim of Automatic Modulation Classification (AMC) is to recognize efficiently the modulation schemes from signals received over UWA channels. In this paper, we propose an efficient method that combines equalization, Gabor filtering of constellation diagrams, thresholding and deep convolutional neural networks (CNNs) for modulation classification over the Signal-to-Noise Ratio (SNR) range of -10 to 30 dB in UWA systems. Simulation results of the proposed method indicated an acceptable performance over a wide SNR range. |
| Keywords: |
| Automatic Modulation Classification (AMC), Underwater Acoustic (UWA), Equalization, Gabor filter, and Constellation diagram |
| Status : Paper Accepted |
