Automatic Modulation Recognition Method Based on Multimodal Time-Frequency Feature Fusion

Abstract

Automatic modulation recognition (AMR) is a key technology in cognitive radio and has a wide range of applications in wireless communication.Aiming at the problem that most of the existing automatic modulation classification methods only use the single-modal information in the time domain or frequency domain,ignoring the complementarity between the multi-modal information,a signal modulation classification recognition method based on multimodal time-frequency feature fusion is proposed.First,the time-domain features and frequency-domain features of the signal are aligned by contrastive learning before fusion to reduce the heterogeneity difference.Secondly,cross-modal attention is used to achieve complementary fusion of time-domain features and frequency-domain features.Finally,in order to further improve the overall performance of the model,a residual shrin-kage module is introduced into the frequency domain encoder to extract the frequency domain features of the time-frequency map and the complex bidirectional gated recurrent unit is introduced into the time domain encoder to extract the correlation features between the I and Q signals and the time-domain features.Experimental results on RadioML2016a show that the proposed me-thod has higher recognition accuracy and noise robustness.

Keywords

• automatic modulation recognition|cross-modal attention fusion|contrastive learning|residual shrinkage module|complex bidirectional gate recurrent unit