A Hybrid Neural Network for Fast Automatic Modulation Classification

Abstract

Automatic modulation classification (AMC) plays a key role in cognitive radio. For AMC, convolutional neural networks (CNNs) have been explored in previous works extensively and deliver the best performance. However, temporal dependencies of signals modeled by CNNs are inherently implicit and insufficient. As a result, models need more data to learn discriminative features automatically. In this work, we propose a hybrid model named HybridNet, where a bidirectional gated recurrent unit (Bi-GRU) is placed after CNN to capture temporal dependencies explicitly. In addition, we investigate why varying Signal-to-Noise Ratio (SNR) dataset makes performance deteriorate. By visualization, we discover that the increase of the intra-class divergence under sharply varying SNR is the central cause. To this end, channel-wise attention is adopted in HybridNet to learn different patterns existing in SNR, which does not require SNR labels in the training process or inference values of SNR. On RadioML2016.10b, our HybridNet obtains the best accuracy among all scales of training data. Especially, in small datasets, our model obtains 87.4% accuracy that is 9.7% higher than the baseline method.

Keywords

• Cognitive radio
• modulation recognition
• deep learning
• signal-to-noise ratio