Boosting Automatic Modulation Recognition in Wireless Communications with Frequency Encoder

Automatic modulation recognition (AMR) plays a crucial role in wireless communications. Deep learning-based AMR methods have garnered significant attention due to their high accuracy. Among these, transformer-based models hold great promise but often underperform in noisy environments. To address this problem, we propose a specific transformer model tailored for signal data, named radio transformer (RaT), which aims to extract frequency-domain features to achieve higher classification accuracy. We design a frequency encoder that contains an adaptive filter to assign weights to each frequency component. Additionally, we introduce global average pooling (GAP) in the classification stage to reduce the impact of noise. Furthermore, we analyze the model’s representational capability with varying slice lengths and encoder layers, identifying the optimal configurations for both parameters. Experimental results on the RML 2018.01A dataset demonstrate that our proposed RaT model achieves superior performance, particularly on low signal-to-noise (SNR) conditions. The accuracy is improved by up to 15.79% when the SNR is 4 dB, underscoring its effectiveness and potential for real-time communication applications.