Complex-valued Parallel Convolutional Recurrent Neural Networks for Automatic Modulation Classification
Yihui Ren, Wen Jiang, Ying Liu
Abstract
Following the great success of deep learning in signal processing, Many models based on real-valued convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have been proposed for automatic modulation classification (AMC). However, the modulation signal is not only temporally dependent but also complex-valued data. The real-valued deep learning models treat the real and imaginary parts of the complex-valued modulation signal as two independent real-valued inputs, which destroy the structure of the raw signal data and make the model more uninterpretable. Thus, this paper proposes a novel complex-valued parallel convolutional recurrent neural network (CPCRNN) specifically for AMC. CPCRNN combines parallel complex-valued CNN and RNN with redesigned complex-valued activation function and complex-valued max pooling. The model directly feeds the complex-valued raw signal to the complex-valued CNN to obtain the complex-valued feature maps, which are then transformed into amplitude and phase and fed to the RNN. Our model can first extract the complex-valued features of the modulation signal with complex-valued CNNs, and then extract the temporal features of the modulation signal with RNNs. CPCRNN achieved an overall accuracy of 62.29% and 69.93% on the benchmark datasets RadioML2016.10A and RadioML2018.01-simple, respectively, outperforming all the state-of-the-art algorithms.