Automatic Modulation Recognition in Time-Varying Channels Combining Manifold Learning and Deep Learning

‍ ‍Automatic modulation recognition has played an important role in both military and civilian fields. Most of the existing researches are based on Gaussian white noise channels， but automatic modulation recognition in time-varying channels is more realistic and challenging. This paper proposes an automatic modulation recognition method that integrates manifold learning and deep learning for time-varying channels. For the first time， the Grassmann manifold is introduced into the feature extraction of the signal， and the signal constellation is modeled to the Grassmann manifold to extract feature.The classification network is composed of two parts based on manifold learning and deep learning，The manifold data is first reduced by the manifold learning network， then mapped to the smooth subspace，finally classified by a simple convolutional neural network. The experimental results show that compared with the traditional convolutional neural network， the proposed scheme has good performance， and at the same time provides a new solution for automatic modulation recognition.