Work

An Efficient Hybrid Deep Learning Model Based on Bi-LSTM and CNN for Automatic Modulation Classification

Year: 2025

Type: article

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.

Source: Sohag Engineering Journal/Sohag Engineering Journal

DOI: 10.21608/sej.2025.415483.1083

Authors: hesham mohamed Ismail, Mostafa Salah Abd-elhafeez, Safwat Mohamed Ramzy

Institutions: Egyptian e-Learning University, Sohag University

Language: English

Topic: Wireless Signal Modulation Classification

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $120