Automatic Modulation Classification Based on Deep Residual Networks With Multimodal Information

Abstract

Automatic modulation classification (AMC) is becoming increasingly important for its fundamental role in dynamic spectrum access, which can support 5G wireless communications to refarm the spectrum resource with low utilization. In order to achieve a better classification performance, several AMC methods based on prototype and variant of convolutional neural networks (CNNs) have been proposed. However, most existing AMC methods based on CNNs only use monomodal information from either time domain or frequency domain. The complementary processing gain, which can be obatianed by fusing multimodal information from multiple transformation domain together, is neglected. To address the issue, we exploit a waveform-spectrum multimodal fusion (WSMF) method to realize AMC based on deep residual networks (Resnet). After extracting features from multimodal information using Resnet, we adopt a feature fusion strategy to merge multimodal features of signals to obtain more discriminating features. Simulation results demonstrate the superior performance of our proposed WSMF method compared with traditional CNNs based AMC method using single modality information. Our proposed method can distinguish among sixteen modulation signals, and it works well even for higher-order digital modulation types like 256QAM and 1024QAM.