MIIF-Net: An Automatic Modulation Classifier Based on Multidimensional Images

Automatic modulation classification (AMC) plays a vital role in Internet of Things (IoT) and Cognitive Radio (CR) to ensure communication security, but the AMC technology is suffering from low signal-to noise ratio (SNR), non-ideal defects in communication systems, and so forth. In order to suppress the impact of interferences on AMC and improve classification performance, we propose a novel multi-dimensional image-based AMC method: Multi-dimensional Image Information Fusion Network (MIIF-Net). Based on the analysis of signal model, this work focuses on suppressing noise and carrier frequency offset (CFO) to improve the classification accuracy of AMC. Motivated by multi-dimensional feature strategy, modulation signals are converted and encoded into multi-dimensional images to introduce multi-domain information and suppress CFO. From the perspective of image enhancement, a novel noise suppression method is proposed, which does not rely on any prior information about signals. In addition, residual network (ResNet) is utilized as encoder to extract distinctive feature in different dimensions, and channel attention mechanism (CAM) is employed to fuse multi-dimensional features. The multi-layer perceptron is utilized for modulation classification in MIIF-Net. MIIF-Net is evaluated in public dataset. Simulation results demonstrate that the proposed MIIF-Net achieves superior performance, especially under low SNR levels. MIIF-Net is further evaluated in practical acquired data. The experimental result shows that MIIF-Net exhibits the consistent advantage under low SNR levels.