Spatial Signal Strength Prediction using 3D Maps and Deep Learning

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 fie...

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Bibliographic Details
Main Authors: Krijestorac, Enes, Hanna, Samer, Cabric, Danijela
Format: Conference Proceeding
Language:English
Subjects:
Deep learning
Prediction algorithms
Software
Software algorithms
Stochastic processes
Three-dimensional displays
UAV
Urban areas
wireless signal strength
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Summary: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.
ISSN:1938-1883
DOI:10.1109/ICC42927.2021.9500970