Research on nonlinear compensation technology in visible light communication system based on improved BP neural network

Visible Light Communication (VLC) technology solves the problem of limited spectrum in traditional mobile communications, and the data transmission rate can reach hundreds of megabits. However, nonlinear distortion also exists in visible light communication systems. Nonlinear distortion will cause a nonlinear relationship between the transmitted signal and the received signal, resulting in a higher bit error rate when the signal is decoded at the receiving end. In order to improve the performance of the VLC system, it is necessary to suppress the nonlinear distortion generated by the signal during the transmission process. Based on the understanding of commonly used VLC system models, LED nonlinear models and two common neural network models, this paper studies a post-distortion compensation algorithm based on a hybrid algorithm of genetics and particle swarm and a Back Propagation (BP) neural network. The received signal of the system is sent to the network, and the original transmitted signal is used as the desired output. The initial weight of the BP network is pre-optimized through a hybrid algorithm of genetics and particle swarm, and then the transmitted signal is estimated with the help of the trained network to achieve distortion compensation. Experimental results show that the improved optimization algorithm. It can effectively suppress the nonlinear characteristics of the VLC system, and appropriately expand the hidden layer of the network to improve the nonlinear mapping ability of the network to a certain extent, so that the compensated signal can better fit the original input signal, which improves the system performance.