UAVradio: Radio link path loss estimation for UAVs
Fecha
2024Autor
Versión
Acceso abierto / Sarbide irekia
Tipo
Artículo / Artikulua
Versión
Versión publicada / Argitaratu den bertsioa
Identificador del proyecto
Impacto
|
10.1016/j.softx.2023.101628
Resumen
The UAVRadio Python module is a comprehensive toolkit designed to facilitate the analysis and prediction of
radio signal path loss in Unmanned Aerial Vehicle (UAV) communication scenarios. The module encompasses
a range of path loss models referenced from established literature, offering users a powerful and flexible
framework for estimating signal attenuation in different UAV communication li ...
[++]
The UAVRadio Python module is a comprehensive toolkit designed to facilitate the analysis and prediction of
radio signal path loss in Unmanned Aerial Vehicle (UAV) communication scenarios. The module encompasses
a range of path loss models referenced from established literature, offering users a powerful and flexible
framework for estimating signal attenuation in different UAV communication links. It is a versatile and modular
tool that enables simple integration for optimizing UAV communication systems and ensuring reliable wireless
connectivity in a variety of operational scenarios. The utility of this package is demonstrated through two
relevant examples: an experimentally fit model comparison with other implemented models, and a UAV
digital twin implementation example comparing different available models and frequencies. The examples
are provided in the code repository along with comprehensive documentation. [--]
Materias
UAV,
Radio,
Path loss,
Simulation,
Model,
Communications
Editor
Elsevier
Publicado en
SoftwareX, 25 (2024) 101628
Departamento
Universidad Pública de Navarra. Departamento de Estadística, Informática y Matemáticas /
Nafarroako Unibertsitate Publikoa. Estatistika, Informatika eta Matematika Saila /
Universidad Pública de Navarra. Departamento de Ingeniería Eléctrica, Electrónica y de Comunicación /
Nafarroako Unibertsitate Publikoa. Ingeniaritza Elektrikoa, Elektronikoa eta Telekomunikazio Ingeniaritza Saila /
Universidad Pública de Navarra/Nafarroako Unibertsitate Publikoa. Institute of Smart Cities - ISC
Versión del editor
Entidades Financiadoras
This work was supported in part by Grant No. RYC2021-
031949-I funded by MCIN/AEI/10.13039/501100011033 and
NextGenerationEU/PRTR; in part by the Ministerio de Ciencia e Innovación
(Spain) under the research grant CONDOR-Connected PID2021-
127409OB-C31; and in part by the Government of Navarre (Departamento
de Desarrollo Económico) under the research grant PC109-110
NAITEST.