Spatial Signal Strength Prediction using 3D Maps and Deep Learning

TL;DR

This paper presents a novel deep learning approach for predicting radio signal strength in urban environments using 3D maps, without needing transmitter location or side channel information, aiding in optimal UAV placement and network planning.

Contribution

It introduces the first method leveraging 3D maps for spatial signal prediction that does not require transmitter location or shadowing parameters.

Findings

Accurately predicts signal strength in complex urban environments.

Does not depend on transmitter location or side channel data.

Applicable to UAV placement and network optimization.

Abstract

Machine learning (ML) and artificial neural networks (ANNs) have been successfully applied to simulating complex physics by learning physics models thanks to large data. Inspired by the successes of ANNs in physics modeling, we use deep neural networks (DNNs) to predict the radio signal strength field in an urban environment. Our algorithm relies on samples of signal strength collected across the prediction space and a 3D map of the environment, which enables it to predict the scattering of radio waves through the environment. While already extensive body of research exists in spatial signal strength prediction, our approach differs from most existing approaches in that it does not require the knowledge of the transmitter location, it does not require side channel information such as attenuation and shadowing parameters, and it is the first work, to the best of our knowledge, to use 3D maps to accomplish the task of signal strength prediction. This algorithm is developed for the purpose of placement optimization of a UAV or mobile robot to maximize the signal strength to or from a stationary transceiver but it also holds relevance to dynamic spectrum access networks, cellular coverage design, power control algorithms, etc.