Carrier Frequency Offset Estimation Based on Signal Denoising and Dual-Channel Convolutional Attention Network Under Low Signal-to-Noise Ratio

In non-cooperative wireless communications, carrier frequency offset (CFO) arises from oscillator mismatch, doppler effect and time-varying channels. Along with noise, they can degrade signal quality and hampers subsequent signal processing. In this letter, we propose a robust carrier frequency offset estimation (CFOE) method under low signal-to-noise ratio (SNR) by leveraging hybrid denoising scheme and dual-channel convolutional attention network (DCA). To enhance noise robustness, we develop a hybrid denoising scheme combining adaptive dual-tree complex wavelet transform with phase-space reconstruction. The designed DCA model can extract deep features from the multi-domain features of the denoised signal. It employs an attention-based parallel dual-pooling mechanism to preserve local-global feature correlation during downsampling. In addition, the composite loss function is used to improve generalization ability and reduce overfitting. Compared with existing blind estimation methods, the experimental results demonstrate the effectiveness and robustness of the proposed CFOE method.