Neural networks applications for the prediction of propagation path loss in urban environments

Popescu, I; Nafornita, I; Constantinou, P; Kanatas, A; Moraitis, N

dc.contributor.author	Popescu, I	en
dc.contributor.author	Nafornita, I	en
dc.contributor.author	Constantinou, P	en
dc.contributor.author	Kanatas, A	en
dc.contributor.author	Moraitis, N	en
dc.date.accessioned	2014-03-01T02:41:56Z
dc.date.available	2014-03-01T02:41:56Z
dc.date.issued	2001	en
dc.identifier.issn	07400551	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/30686
dc.subject	Neural Model	en
dc.subject	Neural Network Application	en
dc.subject	Path Loss	en
dc.subject	Regression Model	en
dc.subject	Root Mean Square Error	en
dc.subject	Standard Deviation	en
dc.subject	Urban Environment	en
dc.subject	Line of Sight	en
dc.subject	Mean Error	en
dc.subject	Non Line of Sight	en
dc.subject	Neural Network	en
dc.subject.other	Algorithms	en
dc.subject.other	Computer simulation	en
dc.subject.other	Conformal mapping	en
dc.subject.other	Error analysis	en
dc.subject.other	Function evaluation	en
dc.subject.other	Mobile radio systems	en
dc.subject.other	Multilayer neural networks	en
dc.subject.other	Radial basis function networks	en
dc.subject.other	Radio transmission	en
dc.subject.other	Regression analysis	en
dc.subject.other	Propagation path loss	en
dc.subject.other	Root mean square error	en
dc.subject.other	Single regression model	en
dc.subject.other	Urban environments	en
dc.subject.other	Signal filtering and prediction	en
dc.title	Neural networks applications for the prediction of propagation path loss in urban environments	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/VETECS.2001.944870	en
heal.identifier.secondary	http://dx.doi.org/10.1109/VETECS.2001.944870	en
heal.publicationDate	2001	en
heal.abstract	This paper presents neural network based models for the prediction of propagation path loss in urban environment. The neural networks are designed separately for line-of-sight (LOS) and non-line-of-sight (NLOS) cases. The performance of the neural model is compared to that of the COST231-Walfisch-Ikegami model, the Walfisch-Bertoni model and the single regression model, based on the absolute mean error, standard deviation and the root mean squared error between predicted and measured values.	en
heal.journalName	IEEE Vehicular Technology Conference	en
dc.identifier.doi	10.1109/VETECS.2001.944870	en
dc.identifier.volume	1	en
dc.identifier.issue	53ND	en
dc.identifier.spage	387	en
dc.identifier.epage	391	en