Robust Automatic Modulation Classification via a Lightweight Temporal Hybrid Neural Network.

In the rapidly developing field of wireless communications, the precise classification of modulated signals is essential for optimizing spectrum utilization and improving communication quality. However, existing networks face challenges in robustness against signals containing phase shift keying and computational efficiency. This paper introduces TCN-GRU, a lightweight model that combines the advantages of multiscale feature extraction of the temporal convolutional network (TCN) and global sequence modeling of gated recurrent unit (GRU). Compared to the state-of-the-art MCLDNN, TCN-GRU reduces parameters by 37.6%, achieving an accuracy of 0.6156 and 0.6466 on the RadioML2016.10a and RadioML2016.10b, respectively (versus MCLDNN's 0.6101 and 0.6462). Furthermore, TCN-GRU demonstrates superior ability in distinguishing challenging modulations such as QAM16 and QAM64, and it improves classification accuracy by about 10.5% compared to MCLDNN. These results suggest that TCN-GRU is a robust and efficient solution for enhancing AMC in complex and noisy environments.