Pathloss Prediction using Deep Learning with Applications to Cellular Optimization and Efficient D2D Link Scheduling

In this paper we propose a highly efficient and very accurate method for estimating the propagation pathloss from a point x to all points y on the 2D plane. Our method, termed RadioUNet, is a deep neural network. For applications such as user-cell site association and device-to-device (D2D) link scheduling, an accurate knowledge of the pathloss function for all pairs of locations is very important. Commonly used statistical models approximate the pathloss as a decaying function of the distance between the points. However, in realistic propagation environments characterized by the presence of buildings, street canyons, and objects at different heights, such radial-symmetric functions yield very misleading results. In this paper we show that properly designed and trained deep neural networks are able to learn how to estimate the pathloss function, given an urban environment, very accurately and extremely quickly. Our proposed method generates pathloss estimations that are very close to estimations given by physical simulation, but much faster. Moreover, experimental results show that our method significantly outperforms previously proposed methods based on radial basis function interpolation and tensor completion.