Automatic Modulation Recognition Using Deep Learning Architectures
Meng Zhang, Yuan Zeng, Zidong Han, Yi Gong · 2018
In this paper, we present an automatic modulation recognition framework for the detection of radio signals in a communication system. The framework considers both a deep convolutional neural network (CNN) and a long short term memory network. Further, we propose a pre-processing signal representation that combines the in-phase, quadrature and fourth-order statistics of the modulated signals. The presented data representation allows our CNN and LSTM models to achieve 8% improvements on our testing dataset. We compare the recognition accuracy of the proposed recognition methods with existing methods under various SNR values. Experimental results show that our methods perform better than the existing methods.