A Study on the Prediction Method for Spatiotemporal Channel Parameters by Convolutional Neural Network using a Spherical Image

In order to realize a wireless emulator, a propagation model with high accuracy adapted for each specific environment is required. A stochastic propagation model is mainly used for the modelling of spatiotemporal propagation characteristics. However, a stochastic propagation model cannot predict accurately because no consideration is given to the surrounding environment. Therefore, a site-specific propagation model considering the surrounding environment is required to realize a wireless emulator. The prediction of multipaths is important for emulating of spatiotemporal parameters because spatiotemporal parameters are affected from multipaths. In particular, multipaths with one reflection or diffraction arriving via a wall or an edge of visible buildings from a transmitter or a receiver have a larger impact than other multipaths. Reflection and diffraction of these multipaths occurred on visible buildings from a transmitter or a receiver. In this paper, the site-specific propagation model for spatiotemporal parameters by a convolutional neural network (CNN) using a spherical image is proposed. The proposed model can predict spatiotemporal parameters because the spherical image just describes visible buildings from a transmitter and a receiver. It was clarified by evaluation that prediction by the proposed model can provide a correct description regarding both statistical characteristics in the evaluation area and specific characteristics at each point. We clarified the effectiveness of the proposed model by comparing it with the conventional model.