A Deep Learning Approach for Modulation Recognition via Exploiting Temporal Correlations
Yanlun Wu, Xingjian Li, Jun Fang · 2018
We consider the problem of automatic modulation recognition for either digital or analogue modulation types. The receiver does not have any knowledge about the modulation type of the received signal, and the objective of this paper is to develop a deep learning approach that can automatically recognize the modulation type of the received signal. The performance of most existing automatic modulation recognition algorithms is highly dependent on the choice of key features and classifiers. In this paper, we resort to a deep learning approach to improve the robustness for modulation recognition. Specifically, to efficiently explore the temporal and spatial correlation, we construct a deep neural network consisting of a convolutional neural network followed by a long short-term memory as the classifier. Our experimental results show that the proposed network architecture achieves a higher classification accuracy than a convolutional neural network architecture that exploits only the spatial correlation of the signal.