DRaGon: Mining Latent Radio Channel Information from Geographical Data Leveraging Deep Learning

Benjamin Sliwa, Melina Geis, Caner Bektas, Melisa Maria Lopez Lechuga, Preben E. Mogensen, Christian Wietfeld

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review


Radio channel modeling is one of the most fundamental
aspects in the process of designing, optimizing, and
simulating wireless communication networks. In this field, longestablished
approaches such as analytical channel models and ray
tracing techniques represent the de-facto standard methodologies.
However, as demonstrated by recent results, there remains an
untapped potential to innovate this research field by enriching
model-based approaches with machine learning techniques. In
this paper, we present Deep RAdio channel modeling from
GeOinformatioN (DRaGon) as a novel machine learning-enabled
method for automatic generation of Radio Environmental Maps
(REMs) from geographical data. For achieving accurate path loss
prediction results, DRaGon combines determining features extracted
from a three-dimensional model of the radio propagation
environment with raw images of the receiver area within a deep
learning model. In a comprehensive performance evaluation and
validation campaign, we compare the accuracy of the proposed
approach with real world measurements, ray tracing analyses,
and well-known channel models. It is found that the combination
of expert knowledge from the communications domain and the
data analysis capabilities of deep learning allows to achieve
a significantly higher prediction accuracy than the reference
TitelIEEE Wireless Communications and Networking Conference 2022
StatusAccepteret/In press - 2022


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