Blind recognition of channel codes based on a multiscale dilated convolution neural network

Abstract

Channel coding is an essential part of communication. In a non-cooperative communication system, the receiver has to recover the signal without any priori knowledge. Therefore, it is important to recognize channel code types and parameters. At present, only a few studies focus on channel code type recognition. In this paper, a multiscale dilated convolution neural network (MSDCNN) is proposed to achieve fine-grained recognition of seven commonly used channel code types. MSDCNN has three multiscale modules. Based on the characteristics of each channel code type, the first two identical modules are designed to extract the overall features of codewords with unit convolution and three parallel regular convolution layers. To lower complexity, the third module uses dilated convolution instead of regular convolution to extract features between codewords. Simulation results show that MSDCNN has higher average recognition accuracy than other neural networks. More importantly, MSDCNN lowers complexity, making it more suitable for deployment so that it can be applied in practical non-cooperative receivers.