Wavelet-based Adaptive Network for Automatic Modulation Recognition under Low SNR

Automatic Modulation Recognition (AMR) plays a pivotal role in modern mobile communications and the advancement of B5G and 6G technologies. However, the progressively intricate and hostile electromagnetic environments pose challenges to modulation recognition. While deep learning can solve complex problems, Digital Signal Processing (DSP) is interpretable and can be more computationally efficient. To combine both, we propose a Wavelet-based Adaptive modulation recognition Network (WAN) specifically designed for low SNR conditions. Diverging from traditional methods that preprocess signals prior to neural network input, our novel approach facilitates mutual synergy between DSP and the neural network during the training phase. We introduce two sub-blocks [Wavelet Threshold Estimate Block (WTEB), Selective Multi-scale Feature Extraction Block (SMFB)], which enable adaptive wavelet transform utilization for extracting multi-scale modulation features from recovery signals. WAN significantly enhances modulation recognition accuracy in low SNR while concurrently augmenting the interpretability of the neural network. Experimental results demonstrate that WAN outperforms SOTA methods in recognition accuracy.