An Efficient Hybrid Deep Learning Model Based on Bi-LSTM and CNN for Automatic Modulation Classification | ||
| Sohag Engineering Journal | ||
| Articles in Press, Accepted Manuscript, Available Online from 26 October 2025 | ||
| Document Type: Original Article | ||
| DOI: 10.21608/sej.2025.415483.1083 | ||
| Authors | ||
| hesham mohamed Ismail* 1; Mostafa Salah Abd-elhafeez2; Safwat Mohamed Ramzy3 | ||
| 1Faculty of Computers and Information Technology, EELU, Giza | ||
| 2Electrical Engineering Department, Faculty of Engineering, Sohag University, Sohag, Egypt | ||
| 3sohag university faculty of engineering | ||
| Abstract | ||
| Automatic modulation classification (AMC) is vital in cognitive radio, spectrum management, and military applications. The rapid evolution of wireless networks demands reliable and effective techniques. Traditional AMC methods, like likelihood-based and feature-based algorithms, face limits in computational complexity and generalization across different channel conditions. This paper combines Bidirectional Long Short-Term Memory (Bi-LSTM) and Convolutional Neural Networks (CNN) to form a novel hybrid deep learning model. The model exploits each branch’s strengths for temporal and spatial feature extraction. The Bi-LSTM branch identifies long-term temporal relationships, such as phase transitions, in signal sequences. In contrast, the CNN branch captures local spatial patterns like constellation structures. The proposed model outperforms baseline models—CLDNN, CNN-GRU, ResNet-LSTM, and VTCNN2—while maintaining lower computational complexity (410,762 parameters) on the RML2016.10b dataset. It achieves a classification accuracy of 92.32% at 18 dB SNR and performs robustly across low-to-high SNR regimes. These results reveal its potential for deployment in real-time wireless scenarios that are both noisy and dynamic. | ||
| Keywords | ||
| Convolutional Neural Networks (CNN); Hybrid Architectures -; Wireless Communication; Bidirectional LSTM; Wireless Signal Classification | ||
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