An intelligent impulsive noise mitigation with deep learning method

To enable message transmission among sensors and equipment, power line communication (PLC) is a widely adopted smart grid. However, due to the occurrence of impulsive noise (IN), reliable transmissions over PLC channels in the smart grid are challenging. Therefore, in this paper, we propose an adaptive noise mitigation scheme to clip the IN with the sliding window-based method, where the altitude of the received signal in the current time slots is obtained by computing the average altitude of signals in the previous and next time slots. To detect the states of IN and dynamically estimate the power threshold of signals for the IN mitigation scheme, we develop an intelligent algorithm based on the long short-term memory network. To prevent the useful signals from being eliminated as IN signals, we propose the accelerated proximal gradient method (APGM) based on tone reservation to reduce the peak-to-average power ratio (PAPR) for the transmitting signals with low computational complexity. In addition, the closed-form expression of the bit error rate (BER) is derived for the proposed sliding window-based IN mitigation scheme according to the probability density function of the IN. Simulation results demonstrate that the proposed IN mitigation scheme achieves a better BER performance than the conventional IN mitigation schemes. In addition, the APGM aided by IN mitigation can further improve BER performance due to the PAPR reduction.