Automatic modulation classification (AMC) is one of the core technologies in non-cooperative communication. In the complex wireless environment, it is not easy to quickly and accurately recognize the modulation styles of signals by conventional methods. The deep learning method (DLM) can deal with the problem and achieve good effects. In conventional DLMs, the length of input data is fixed. However, the signal length in communication is changing, which may not make full use of the DLMs’ input signal information to improve the recognition accuracy. In this paper, the deep multi-hop convolutional neural network (CNN) is employed to learn the time-domain signal features with different lengths. The proposed network includes the multi-hop connection rate and the receptive field extension scope to dispose of the limitation. The experiment shows that the proposed network can achieve better recognition results under the sparse multi-hop network structure. The reception field extension scope is also conducive to further improve the recognition effects. Finally, the proposed network has shorter training time and smaller parameters, which is more convenient for training the network and deploying in the existing communication system.

自动调制分类（AMC）是非合作通信的核心技术之一。在复杂的无线环境中，传统的方法很难快速准确地识别出信号的调制方式。深度学习方法（DLM）可以处理问题并取得良好的效果。在传统的 DLM 中，输入数据的长度是固定的。然而，通信中的信号长度不断变化，可能无法充分利用DLM的输入信号信息来提高识别精度。本文采用深度多跳卷积神经网络（CNN）来学习不同长度的时域信号特征。建议的网络包括多跳连接速率和接收域扩展范围来处理限制。实验表明，本文提出的网络在稀疏多跳网络结构下能取得较好的识别效果。接收场扩展范围也有利于进一步提高识别效果。最后，所提出的网络具有更短的训练时间和更小的参数，更便于在现有通信系统中训练网络和部署。