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

For beyond 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 paper, we propose a novel path loss model based on multi-dimensional Gaussian process regression (GPR) that gives 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 a local shadow fading term. In contrast to the log-distance path loss model and other regression-based approaches, the special structure of the proposed model provides good spatial consistency. Moreover, since the proposed model is based on GPR, it provides the uncertainty of the predicted path loss. We validate the performance of the proposed model in terms of prediction accuracy with the measurement datasets from two different indoor environments. Our experiments show that the proposed model predicts better than the log-distance path loss model, especially when spatial correlation gets more significant. The proposed model can be also used to simulate path loss in a general environment after training the measurement data.