Deep learning is used for automatic modulation recognition in neural networks, and because of the need for high classification accuracy, deeper and deeper networks are used. However, these are computationally very expensive for neural network training and inference, so its utility in the case of a mobile with memory limitations or weak computational power is questionable. As a result, a trade-off between network depth and network classification accuracy must be considered. To address this issue, we used a knowledge distillation method in this study to improve the classification accuracy of a small network model. First, we trained Inception–Resnet as a teacher network, which has a size of 311.77 MB and a final peak classification accuracy of 93.09%. We used the method to train convolutional neural network 3 (CNN3) and increase its peak classification accuracy from 79.81 to 89.36%, with a network size of 0.37 MB. It was also used similarly to train mini Inception–Resnet and increase its peak accuracy from 84.18 to 93.59%, with a network size of 39.69 MB. When we compared all classification accuracy peaks, we discover that knowledge distillation improved small networks and that the student network had the potential to outperform the teacher network. Using knowledge distillation, a small network model can achieve the classification accuracy of a large network model. In practice, choosing the appropriate student network based on the constraints of the usage conditions while using knowledge distillation (KD) would be a way to meet practical needs.
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