Robust and Efficient Modulation Recognition with Pyramid Signal Transformer

A robust and efficient pyramid signal Transformer model, called SigFormer for automatic modulation recognition was proposed in this paper. In SigFormer, a pyramid Transformer architecture is introduced to encode the relationship between the internal features of modulated signals. Specifically, a dual-attention block composed of self-attention layer and scaling-attention layer is proposed for simultaneous global feature representation and noise resistance learning for modulated signals, and small-kernel convolution layers embedded to dual-attention block and feed-forward block is proposed for fine-grained modulation recognition as well. Experiments on RML2018.01a, RML2016.10a and RML2016.10b show that the SigFormer outperformed most other deep learning models on recognition accuracy, and it is more parameter-efficient than most other models and more robust on low signal-to-noise ratio (SNR) signals.