Predictive Modeling of Signal Degradation in Urban VANETs Using Artificial Neural Networks

In urban Vehicular Ad Hoc Network (VANET) environments, buildings play a crucial role as they can act as obstacles that attenuate the transmission signal between vehicles. Such obstacles lead to multipath effects, which could substantially impact data transmission due to fading. Therefore, quantifyi...

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Vydáno v:	Electronics (Basel) Ročník 12; číslo 18; s. 3928
Hlavní autoři:	Muktar, Bappa, Fono, Vincent, Zongo, Meyo
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Basel MDPI AG 01.09.2023
Témata:	Accuracy Ad hoc networks (Computer networks) Algorithms Artificial intelligence Artificial neural networks Back propagation networks Barriers Bit error rate Buildings Communication Computer simulation Conjugate gradient method Correlation coefficients Data collection Data mining Data transmission Decisions Deep learning Degradation Digital mapping Emergency vehicles Geospatial data Machine learning Mathematical models Mobile ad hoc networks Neural networks Packet transmission Parameters Prediction models Regularization Root-mean-square errors Routing (telecommunications) Signal processing Simulation Spatial data Vehicles Canada
ISSN:	2079-9292, 2079-9292
On-line přístup:	Získat plný text
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