A ray launching-neural network approach for radio wave propagation analysis in complex indoor environments
Fecha
2014Autor
Versión
Acceso abierto / Sarbide irekia
Tipo
Artículo / Artikulua
Versión
Versión aceptada / Onetsi den bertsioa
Impacto
|
10.1109/TAP.2014.2308518
Resumen
A novel deterministic approach to model the radio
wave propagation channels in complex indoor environments
reducing computational complexity is proposed. This technique
combines a neural network and a 3D ray launching algorithm in
order to compute wireless channel performance in indoor
scenarios. An example of applying the method for studying indoor
radio wave propagation is presented and t ...
[++]
A novel deterministic approach to model the radio
wave propagation channels in complex indoor environments
reducing computational complexity is proposed. This technique
combines a neural network and a 3D ray launching algorithm in
order to compute wireless channel performance in indoor
scenarios. An example of applying the method for studying indoor
radio wave propagation is presented and the results are compared
with a very high resolution fully three dimensional ray launching
simulation as the reference solution. The new method allows the
use of a lower number of launched rays in the simulation scenario
whereas intermediate points can be predicted using neural
network. Therefore a high gain in terms of computational
efficiency (approximately 80% saving in simulation time) is
achieved. [--]
Materias
3D-ray launching,
Neural network,
RF environment modeling,
Radio channel simulation,
Multipath
Editor
IEEE
Publicado en
IEEE Transactions on Antennas and Propagation, 62(5), 2777-278
Departamento
Universidad Pública de Navarra. Departamento de Ingeniería Eléctrica y Electrónica /
Nafarroako Unibertsitate Publikoa. Ingeniaritza Elektrikoa eta Elektronikoa Saila
Versión del editor
Entidades Financiadoras
The authors wish to acknowledge the financial support of
project IIM13185.RI1 FASTER, funded by the Government of
Navarra and project TEC2010-21563-C02-01 ENEIDA,
funded by the Ministry of Science, Spain.