CSCNN: Lightweight modulation recognition model for mobile multimedia intelligent information processing

Abstract With the advancement of the Internet of Things, the importance of multimedia intelligent information processing technology is increasing. Faced with massive electromagnetic data and limited terminal equipment computing resources, previous technologies cannot meet the real-time decisionmaking requirements for processing short-term observations or burst signals in deployable systems. In this paper, our approach proposes the Deep Complex Separable Convolution (DCSC) operation by combining separable convolution operation and complex convolution operation. At the same time, to better preserve coupling information between channels and minimize the model size, we propose the Multilevel Separable Convolutional Residual Block (MSCRB). Based on the above two methods, we constructed the Complex Separable Convolutional Neural Network (CSCNN). This neural network significantly reduces the complexity of the deep learning model. The smallest network we constructed, CSCNN-Tiny, has a model size of 0.760M, which is only 6% of the size of MobileNet. With 0.815M Flops, it is 3.8% of MobileNet. However, it achieves a recognition accuracy of 50.97%, only 0.97% lower than MobileNet.