Research On Power Line Channel Noise Modeling Based On BP Neural Network

Power line channel noise has a great impact on the quality of power line communication(PLC).The accurate establishment of power line channel noise model is of great significance for the study of noise suppression methods.This paper studies the power line channel noise modeling method based on BP(back propagation)neural network.The main work is as follows:Firstly,the limitations of the traditional noise model are analyzed.Through the research on the noise characteristics of PLC channel,the PLC noise acquisition system is built,the measured noise data is processed by the noise separation method,and the impulse noise and background noise are separated,which provides the basis for the study of traditional noise model.Based on the principle of traditional noise modeling,AR model,background noise,Middleton class A and Bernoulli Gaussian impulse noise models are simulated and compared with the actual noise.Secondly,a noise model based on BP neural network is constructed.In the MATLAB simulation environment,the parameters such as the number of network layers,the number of neurons in the hidden layer and the number of training times are determined by function approximation method,calculation mean method and comparison error method respectively,and the BP neural network training model is established,and then the noise model is constructed.The effectiveness of the channel noise model based on BP neural network is verified by comparing and analyzing the time-domain waveform,power spectrum density(PSD),root mean square error,autocorrelation and nonlinear characteristics of the real noise and predicted noise of the model.Finally,aiming at the problems of poor robustness and slow convergence speed of BP neural network,a noise modeling method based on genetic algorithm(GA)and particle swarm optimization(PSO)is proposed to optimize BP neural network.The principles of GA algorithm and PSO algorithm are analyzed,and the PLC channel noise model based on GA-BP and PSO-BP neural network is constructed.For the new noise model,the effectiveness of the noise model is verified by analyzing the evolution times,time domain waveform,PSD,root mean square error and autocorrelation of the noise model built by GA-BP and PSO-BP neural network;By comparing and analyzing the iteration times,training time and the difference between the actual noise and the predicted noise in time and frequency domain,it is verified that the effectiveness of the two noise models is better than that of BP neural network model.