Path Loss Model Based on Machine Learning Using Multi-Dimensional Gaussian Process Regression

Abstract

For beyond the fifth-generation (5G) and future wireless communications, spatial consistency that represents the correlation between propagation channel characteristics in close proximity has become one of the major issues in channel modeling to describe channels more realistically in emerging scenarios, such as device-to-device (D2D). In this study, we propose a novel path loss model based on multi-dimensional Gaussian process regression (GPR) that provides spatial consistency to channels in propagation environment by predicting local shadow fading while fitting large-scale path loss from measured data. The proposed model has a special structure consisting of a radial mean function and local shadow fading term, which are modeled by independent Gaussian Processes. In contrast to the log-distance path loss model and other regression-based models, the proposed model trains two functions simultaneously; thus, predicts path loss well by capturing both global tendency and local correlation. Moreover, because the proposed model is based on GPR, it provides uncertainty of the predicted path loss. We validated the performance of the proposed model in terms of prediction accuracy with the measurement datasets from two different indoor environments. Our experiments showed that the proposed model predicts better than the log-distance path loss model, especially when spatial correlation becomes significant. The proposed model can be used to simulate path loss in a general environment after training the measurement data.