RadioUNet: Fast Radio Map Estimation with Convolutional Neural Networks

TL;DR

RadioUNet is a deep learning approach that accurately and efficiently estimates radio propagation pathloss in urban environments, outperforming previous methods and enabling real-time applications.

Contribution

The paper introduces RadioUNet, a neural network model that learns to estimate pathloss from simulations, providing faster and more accurate predictions in complex urban settings.

Findings

RadioUNet achieves high accuracy close to physical simulations.

It significantly outperforms previous pathloss estimation methods.

The model enables real-time pathloss predictions in urban environments.

Abstract

In this paper we propose a highly efficient and very accurate deep learning method for estimating the propagation pathloss from a point $x$ (transmitter location) to any point $y$ on a planar domain. For applications such as user-cell site association and device-to-device link scheduling, an accurate knowledge of the pathloss function for all pairs of transmitter-receiver locations is very important. Commonly used statistical models approximate the pathloss as a decaying function of the distance between transmitter and receiver. 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, in a very accurate and computationally efficient manner. Our proposed method, termed RadioUNet, learns from a physical simulation dataset, and generates pathloss estimations that are very close to the simulations, but are much faster to compute for real-time applications. Moreover, we propose methods for transferring what was learned from simulations to real-life. Numerical results show that our method significantly outperforms previously proposed methods.