Improved Modulation Classification Based on Hough Transforms of Constellation Diagrams Using CNN for the UWA-OFDM Communication System
The Automatic Modulation Classification (AMC) for underwater acoustic signals enables more efficient utilization of the acoustic spectrum. Deep learning techniques significantly improve classification performance. Hence, they can be applied in AMC work to improve the underwater acoustic (UWA) commun...
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2025-06-01
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| author | Mohamed A. Abdel-Moneim Mohamed K. M. Gerwash El-Sayed M. El-Rabaie Fathi E. Abd El-Samie Khalil F. Ramadan Nariman Abdel-Salam |
| author_facet | Mohamed A. Abdel-Moneim Mohamed K. M. Gerwash El-Sayed M. El-Rabaie Fathi E. Abd El-Samie Khalil F. Ramadan Nariman Abdel-Salam |
| author_sort | Mohamed A. Abdel-Moneim |
| collection | DOAJ |
| description | The Automatic Modulation Classification (AMC) for underwater acoustic signals enables more efficient utilization of the acoustic spectrum. Deep learning techniques significantly improve classification performance. Hence, they can be applied in AMC work to improve the underwater acoustic (UWA) communication. This paper is based on the adoption of Hough Transform (HT) and Edge Detection (ED) to enhance modulation classification, especially for a small dataset. Deep neural models based on basic Convolutional Neural Network (CNN), Visual Geometry Group-16 (VGG-16), and VGG-19 trained on constellation diagrams transformed using HT are adopted. The objective is to extract features from constellation diagrams projected onto the Hough space. In addition, we use Orthogonal Frequency Division Multiplexing (OFDM) technology, which is frequently utilized in UWA systems because of its ability to avoid multipath fading and enhance spectrum utilization. We use an OFDM system with the Discrete Cosine Transform (DCT), Cyclic Prefix (CP), and equalization over the UWA communication channel under the effect of estimation errors. Seven modulation types are considered for classification, including Phase Shift Keying (PSK) and Quadrature Amplitude Modulation (QAM) (2/8/16-PSK and 4/8/16/32-QAM), with a Signal-to-Noise Ratio (SNR) ranging from −5 to 25 dB. Simulation results indicate that our CNN model with HT and ED at perfect channel estimation, achieves a 94% classification accuracy at 10 dB SNR, outperforming benchmark models by approximately 40%. |
| format | Article |
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| issn | 2673-4117 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| series | Eng |
| spelling | doaj-art-3cc76f8a52b84fdab7dc909959b172da2025-08-20T02:24:30ZengMDPI AGEng2673-41172025-06-016612710.3390/eng6060127Improved Modulation Classification Based on Hough Transforms of Constellation Diagrams Using CNN for the UWA-OFDM Communication SystemMohamed A. Abdel-Moneim0Mohamed K. M. Gerwash1El-Sayed M. El-Rabaie2Fathi E. Abd El-Samie3Khalil F. Ramadan4Nariman Abdel-Salam5Department of Telecommunication, Faculty of Engineering, Egyptian Russian University, Cairo 11829, EgyptScience and Innovation Center of Excellence (SICE), Egyptian Russian University, Cairo 11829, EgyptDepartment of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf 32952, EgyptDepartment of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf 32952, EgyptDepartment of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf 32952, EgyptDepartment of Communications and Electronics Engineering, Faculty of Engineering, Canadian International College (CIC), Giza 12511, EgyptThe Automatic Modulation Classification (AMC) for underwater acoustic signals enables more efficient utilization of the acoustic spectrum. Deep learning techniques significantly improve classification performance. Hence, they can be applied in AMC work to improve the underwater acoustic (UWA) communication. This paper is based on the adoption of Hough Transform (HT) and Edge Detection (ED) to enhance modulation classification, especially for a small dataset. Deep neural models based on basic Convolutional Neural Network (CNN), Visual Geometry Group-16 (VGG-16), and VGG-19 trained on constellation diagrams transformed using HT are adopted. The objective is to extract features from constellation diagrams projected onto the Hough space. In addition, we use Orthogonal Frequency Division Multiplexing (OFDM) technology, which is frequently utilized in UWA systems because of its ability to avoid multipath fading and enhance spectrum utilization. We use an OFDM system with the Discrete Cosine Transform (DCT), Cyclic Prefix (CP), and equalization over the UWA communication channel under the effect of estimation errors. Seven modulation types are considered for classification, including Phase Shift Keying (PSK) and Quadrature Amplitude Modulation (QAM) (2/8/16-PSK and 4/8/16/32-QAM), with a Signal-to-Noise Ratio (SNR) ranging from −5 to 25 dB. Simulation results indicate that our CNN model with HT and ED at perfect channel estimation, achieves a 94% classification accuracy at 10 dB SNR, outperforming benchmark models by approximately 40%.https://www.mdpi.com/2673-4117/6/6/127automatic modulation classification (AMC)underwater acoustic (UWA) communicationsHough transform (HT)convolutional neural network (CNN) |
| spellingShingle | Mohamed A. Abdel-Moneim Mohamed K. M. Gerwash El-Sayed M. El-Rabaie Fathi E. Abd El-Samie Khalil F. Ramadan Nariman Abdel-Salam Improved Modulation Classification Based on Hough Transforms of Constellation Diagrams Using CNN for the UWA-OFDM Communication System Eng automatic modulation classification (AMC) underwater acoustic (UWA) communications Hough transform (HT) convolutional neural network (CNN) |
| title | Improved Modulation Classification Based on Hough Transforms of Constellation Diagrams Using CNN for the UWA-OFDM Communication System |
| title_full | Improved Modulation Classification Based on Hough Transforms of Constellation Diagrams Using CNN for the UWA-OFDM Communication System |
| title_fullStr | Improved Modulation Classification Based on Hough Transforms of Constellation Diagrams Using CNN for the UWA-OFDM Communication System |
| title_full_unstemmed | Improved Modulation Classification Based on Hough Transforms of Constellation Diagrams Using CNN for the UWA-OFDM Communication System |
| title_short | Improved Modulation Classification Based on Hough Transforms of Constellation Diagrams Using CNN for the UWA-OFDM Communication System |
| title_sort | improved modulation classification based on hough transforms of constellation diagrams using cnn for the uwa ofdm communication system |
| topic | automatic modulation classification (AMC) underwater acoustic (UWA) communications Hough transform (HT) convolutional neural network (CNN) |
| url | https://www.mdpi.com/2673-4117/6/6/127 |
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