Path loss prediction based on multi-layer perceptron artificial neural network

In order to better serve the network planning and optimization of the 5th generation and future communication systems, the path loss prediction based on multi-layer perceptron (MLP) neural network is carried out in this paper. A simple method to characterize the propagation environment is proposed by the limited clutter type information, avoiding the cumbersome three-dimensional (3D) scenario modeling. Combining with the measurement data and environmental features extracted by the environmental characterization method, the path loss model based on the MLP neural network is established. The comparative analysis of data experiments shows that the MLP neural network can achieve accurate prediction of path loss, and the introduction of environmental features can help improve the performance of the MLP-based path loss model. In order to solve the problem that interference clutters reduce the accuracy of the MLP-based path loss model and this model is sensitive to environmental changes, the environmental characterization method is improved based on the label that can judge whether it is line-of-sight (LoS) or non-line-of-sight (NLoS), which further enhances the stability and generalization ability of the MLP-based path loss model. This paper is designed to understand the propagation characteristics of radio wave, which can provide a theoretical basis for wireless network optimization and communication system design.