Learning Temporal–Spectral Feature Fusion Representation for Radio Signal Classification

With the rapid development of wireless communications, industrial electromagnetic environments are facing challenges in terms of spectrum scarcity and cyberspace threats. Moreover, the coexistence of various types of radio signals within the same frequency band may cause signal distortion and degrade the quality and efficiency of communication. To effectively address these challenges, a novel temporal–spectral feature fusion network (TSFFN) for radio signal classification (RSC) is proposed. TSFFN adopts a Cutmix-based temporal–spectral fusion and an attention-based multiview feature fusion mechanism. These mechanisms automatically learn and merge spectrogram, temporal–spectral, and time-domain features by effectively combining temporal and spectral information into high-dimensional representations. This augmentation enhances the network's ability to discriminate different radio signals, enabling accurate spectrum sensing and signal identification for effective spectrum management. Experimental results on five datasets demonstrate the effectiveness of our approach in enhancing RSC performance across diverse industrial scenarios.