Using Feedforward Neural Networks for Parameter Modeling of a 4G Link for Unmanned Aerial Vehicles

Giancarlo Benincasa, Erich Leitgeb, Klaus Kainrath, Hristo Danchov Ivanov

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandBegutachtung

Abstract

A prediction of link parameters based on positional
information contributes to a safe operation of Unmanned Aerial
Vehicles (UAVs). For logistical and economical reasons, it makes
sense to establish the wireless link via the fourth generation
mobile standard Long Term Evolution (LTE) as it offers an
already existing and stable infrastructure. The usual non line
of sight (NLOS) connection in such a scenario poses challenges
in parameter modeling, for which artificial neural networks are
shown to be a suitable solution in this paper. The connection
quality between transmitter station and UAV is mainly described
by the parameters Reference Signal Received Power (RSRP) and
Reference Signal Received Quality (RSRQ), which are tried to be
predicted via an Artificial Neural Network (ANN) in this work.
This approach takes the UAVs position (either Cartesian or polar
coordinates) as input and maps them to the respective parameter.
Originalspracheenglisch
Titel2021 International Conference on Software, Telecommunications and Computer Networks (SoftCOM)
Herausgeber (Verlag)IEEE Xplore
Seiten1-5
ISBN (elektronisch)1847-358X
DOIs
PublikationsstatusVeröffentlicht - 2021
Veranstaltung2021 International Conference on Software, Telecommunications and Computer Networks: SoftCOM 2021 - Hvar, Kroatien
Dauer: 23 Sep. 202125 Sep. 2021

Konferenz

Konferenz2021 International Conference on Software, Telecommunications and Computer Networks
KurztitelSoftCOM 2021
Land/GebietKroatien
OrtHvar
Zeitraum23/09/2125/09/21

Fields of Expertise

  • Information, Communication & Computing

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