High-Order Modulation Recognition of Communication Signals Based on Feature Fusion

This is the authors' abstract. We don't add key points for this paper.

To address the issue of poor recognition performance of high-order modulation signals under low signal-to-noise ratio conditions, a recognition model based on feature fusion and multi-kernel classifier is designed for an open dataset of high-order modulation signals. Firstly, the advantages of convolutional neural networks and gated recurrent units are fully utilized to extract deep features of high-order modulation signals. Additionally, high-order cumulants of the high-order modulation signals are extracted as supplementary features. Finally, a multi-kernel learning classifier is constructed using multi-kernel linear combination to fuse the aforementioned two types of features, effectively improving the recognition accuracy of high-order modulation under low signal-to-noise ratio conditions. Experimental results demonstrate that the feature fusion recognition model significantly improves the recognition performance of high-order modulation signals under low signal-to-noise ratio conditions, with an average recognition accuracy exceeding 82%.