UNet-INSN: Self-Supervised Algorithm for Impulsive Noise Suppression in Power Line Communication

Impulsive noise suppression plays a crucial role in enhancing the reliability of power line communication (PLC). In view of the rapid advancement of deep learning methodologies, recently, studies on deep learning-based impulsive noise suppression have garnered extensive attention. Nevertheless, on one hand, the training of deep learning-based impulsive noise suppression models relies on a large number of labeled data, whose acquisition incurs high costs. On the other hand, the currently proposed models struggle to adapt to the dynamic variations in impulsive noise distributions. To address these two issues, in this paper, a UNet-based self-supervised learning model for impulsive noise suppression (UNet-INSN) is proposed. Firstly, by using the designed global mask mapper, UNet-INSN can utilize the entire noisy signal for model training, resolving the information loss issue caused by partial signal masking in traditional mask-driven algorithms. Secondly, a reproducibility loss function is introduced to effectively prevent the model from degenerating into an identity mapping, thereby enhancing the denoising performance of UNet-INSN. Simulation results show that the required SNRs for the proposed algorithm to achieve a bit error rate of 10−6 under ideal and non-ideal conditions are 12 dB and 26 dB, respectively, significantly outperforming comparison methods. Moreover, it still exhibits excellent robustness and generalization capabilities when the impulsive noise distribution changes dynamically.