A modulation classification method based on deformable convolutional neural networks for broadband satellite communication systems

In order to solve the problem of broadband satellite modulation signal with SNR fluctuation of complex channel and various modulation signal recognizing, we propose a Deformable Convolutional Neural Networks (DCNN) classification model based on broadband satellite communication systems. In our algorithm, we propose a deformable convolution kernel, which only need to calculate the 2/3 pixel convoluting. Our algorithm not only can be used to reduce the complexity and improve the robustness, but also used to improve the accuracy. We simulate the accuracy and the complexity of the algorithm among the four neural network models of DCNN, VGG, AlexNet and ResNet. The results show that the design of the DCNN model has high recognition rate and low algorithm complexity. Then we simulate the DCNN network in variable signal-to-noise of BPSK, QPSK, 8PSK, 16APSK, 32APSK, 16QAM, 32QAM and 64QAM commonly used satellite modulation signal classification and complex channel conditions, and training the four basic modulation signal used to identify other modulation signals. The results show that the DCNN model not only can be used to maintain a high recognition rate of the modulated signal, but also used to reduce the complexity of the algorithm and improves the robustness of the algorithm.