ANN prediction models for outdoor SIMO millimeter band system

Moraitis, N; Vouyioukas, D

dc.contributor.author	Moraitis, N	en
dc.contributor.author	Vouyioukas, D	en
dc.date.accessioned	2014-03-01T02:44:28Z
dc.date.available	2014-03-01T02:44:28Z
dc.date.issued	2007	en
dc.identifier.issn	15715736	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/31834
dc.subject	Angle of Arrival	en
dc.subject	Artificial Neural Network	en
dc.subject	General Regression Neural Network	en
dc.subject	Mean Square Error	en
dc.subject	Millimeter Wave	en
dc.subject	Multilayer Perceptron	en
dc.subject	Prediction Model	en
dc.subject	Smart Antenna	en
dc.subject	Standard Deviation	en
dc.subject	Single Input Single Output	en
dc.title	ANN prediction models for outdoor SIMO millimeter band system	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1007/978-0-387-74161-1_31	en
heal.identifier.secondary	http://dx.doi.org/10.1007/978-0-387-74161-1_31	en
heal.publicationDate	2007	en
heal.abstract	This paper presents the prediction propagation paths of angle of arrivals (AoAs) of a Smart Antenna System in an outdoor environment utilizing Artificial Neural Networks (ANN). The proposed models consist of a Multilayer Perceptron and a Generalized Regression Neural Network trained with measurements of an antenna system consisted of a Single Input Single Output (SISO) system in the millimeter wave band. For comparison purposes the theoretical Gaussian scatter density model was investigated for the derivation of the power angle profile. The proposed models utilize the characteristics of the environment for prediction of the angle of arrivals of each one of the propagation paths and can be applicable for the derivation of SIMO (Single Input Single Output) parameters, such as system capacity. The results are presented towards the average error, standard deviation and mean square error compared with the measurements and they are capable for the derivation of accurate prediction models for the case of AoA in an outdoor millimeter wave propagation environment. © 2007 International Federation for Information Processing.	en
heal.journalName	IFIP International Federation for Information Processing	en
dc.identifier.doi	10.1007/978-0-387-74161-1_31	en
dc.identifier.volume	247	en
dc.identifier.spage	287	en
dc.identifier.epage	296	en