Exploiting Complex-Valued Representations in Automatic Modulation Recognition: A Framework Integrating a Transformer With Relative Positional Encoding and Separable Convolution

Abstract

Automatic modulation recognition plays a critical role in military applications, particularly in electronic warfare, spectrum surveillance, and secure communication systems. The precise identification of signal modulation modes is crucial for ensuring the efficacy, security, and efficiency of communications. Given the problems of limited feature extraction capability and performance degradation when dealing with low signal-to-noise ratio (SNR) signals, this work proposes a novel model architecture that combines the Transformer with relative position encoding and separable convolution (SC) in the complex domain. The network can directly process the complex representation of signals to capture features in the time-frequency domain and enhance the ability to recognize complex signals. This method introduces relative position encoding in the complex domain into the Transformer framework, which uses complex attention mechanisms and adaptive position encoding to enhance the model’s long-range modeling capability. At the same time, it improves computational efficiency and local feature extraction capability by introducing SC layers. Then, we construct an attention-driven feature fusion module, which can automatically adjust the weight ratio between features to achieve the optimal combination of features. The model shows excellent classification performance under various SNR conditions in RadioML2016.10a and RadioML2016.10b datasets, especially in low SNR environments, which is significantly improved compared to other methods. The research not only provides a new solution for AMR tasks but also expands new ideas for applying the Transformer model in signal processing.