Mixed-Domain Feature Fusion Network for Few-Sample Automatic Modulation Recognition
Bilian Wang, Jingjing Lu · 2024
With the explosion of modern wireless communication information and the need for high transmission rates, automatic modulation recognition (AMR) based on deep learning (DL) has become increasingly popular. However, DL-based AMR methods often require large training samples. In order to solve this problem, we propose a mixed-domain feature fusion network (MDFNet) for small sample modulation recognition. The network can efficiently fuse the time and frequency domain features of signals, have certain noise reduction effect on the feature transmission of signals, enhance the capture of effective information by the model, and greatly improve the identification accuracy. Experiments with on two existing well-known datasets RML2016.10a and RML2016.04c demonstrate that the model performs significantly in small sample modulation identification.