CGDNet: Efficient Hybrid Deep Learning Model for Robust Automatic Modulation Recognition

In this letter, we introduce CGDNet, a cost-efficient hybrid neural network composed of a shallow convolutional network, a gated recurrent unit, and a deep neural network, for robust automatic modulation recognition for cognitive radio services of modern communication systems. Our model employs pooling layers, small filter sizes, Gaussian dropout layers, and skip connections which leads to an increase in network capacity, a reinforced process of feature extraction, and prevents the vanishing gradient problem. From our experiments, CGDNet incurs a low computational complexity and reaches the overall n-modulation recognition accuracy of 93.5% and 90.38% on two widely used Deep-Sig datasets.