Hybrid Deep Learning for Signals Automatic Modulation Classification

Classifying signals or modulation classification is a crucial step in developing communication receivers. A common practice is to extract features before categorizing the signal, which requires implementing long preprocessing techniques. Due to breakthroughs in neural network topologies, machine learning (ML) algorithms, and optimization techniques, referred to as "deep learning" (DL), we have witnessed a vast degree of change over the previous five years. Advanced deep learning algorithms can be applied to the same automatic modulation classification problem and generate excellent outcomes without requiring time-consuming, manual, and complex feature extraction methods. In recent years, various DL techniques have been explored for automatic modulation classification (AMC). However, it has been observed that these techniques are effective only for higher Signal-to-Noise-Ratio (SNR) values. To overcome this challenge, we proposed a hybrid DL-based AMC technique by combining a customized EfficientNet with a customized Transformer Block. The transformer block is used to enhance the DL performance for the lower SNR values. The performance of the proposed hybrid model is tested on a benchmark dataset, RadioML2018.01A, and compared with the state-of-the-art existing DL method which shows the supremacy of the proposed hybrid model.

@article{Moinuddin2025,
  title     = {Hybrid Deep Learning for Signals Automatic Modulation Classification},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {12},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Muhammad Moinuddin and Hitham K. Alshoubaki and Omar Ayad Alani and Ubaid M. Al-Saggaf and Karim Abed-Meraim},
  doi       = {10.14569/IJACSA.2025.01612132},
  url       = {https://doi.org/10.14569/IJACSA.2025.01612132}
}