Impulsive Noise Suppression Network for Power Line Communication

Abstract

This letter proposes a multi-features space domain fusion network (MFSDF-Net) to address impulsive noise suppression issues in power line communication (PLC) systems. As an end-to-end model, the proposed deep learning algorithm eliminates the need for designing null subcarriers in the original signals and fitting the data distribution at the receiver side. By utilizing parallel convolutional kernels to extract and fuse details from different domains of the signals, MFSDF-Net effectively captures dynamic changes. This enables it to more accurately and effectively identify and suppress impulsive noise, thus addressing the shortcomings of existing algorithms that inadequately identify impulsive noise and exhibit the bit error ratio (BER) floor effect. Simulation results show that with perfect channel estimation, the signal-to-noise ratio (SNR) at a BER of 1e-5 is 18 dB for this model, compared to 26 dB or higher for others, indicating an 8 dB improvement. With imperfect channel estimation, this model achieves an SNR of 30 dB at a BER of 1e-5, while other algorithms exhibit a BER floor effect.