A Deep learning approach for the Estimation of Middleton Class-A Impulsive Noise Parameters

Impulsive noise is a common impediment in many wireless, power line communication (PLC), and smart grid communication systems that prevents the system from achieving error-free transmission. To overcome the detrimental effects of such impulsive interference, knowledge of impulsive noise parameters is generally required by the available mitigation techniques. This work considers a machine learning perspective for the estimation of the impulsive noise parameters in communication systems under the influence of Middleton class-A noise. Precisely, we consider a deep learning approach and design a deep neural network (DNN) that classifies a set of received symbols according to the parameters of the impulsive noise affecting them. It is sown that the classification accuracy greatly depends on the number of symbols fed into the neural network as well as the number of considered states in the classification, where the proposed approach can reach a testing accuracy of more than 99%.