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Comparison of neural network models for path loss prediction

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dc.contributor.author Popescu, I en
dc.contributor.author Nafornita, I en
dc.contributor.author Constantinou, P en
dc.date.accessioned 2014-03-01T02:43:10Z
dc.date.available 2014-03-01T02:43:10Z
dc.date.issued 2005 en
dc.identifier.uri http://hdl.handle.net/123456789/31274
dc.subject Channel characterization en
dc.subject Neural networks en
dc.subject Propagation path loss models en
dc.subject.other Electromagnetic wave propagation en
dc.subject.other Error analysis en
dc.subject.other Mathematical models en
dc.subject.other Motion planning en
dc.subject.other Statistical methods en
dc.subject.other Channel characterization en
dc.subject.other Measured path loss en
dc.subject.other Propagation path loss models en
dc.subject.other Root mean square error en
dc.subject.other Feedforward neural networks en
dc.title Comparison of neural network models for path loss prediction en
heal.type conferenceItem en
heal.identifier.primary 10.1109/WIMOB.2005.1512814 en
heal.identifier.secondary http://dx.doi.org/10.1109/WIMOB.2005.1512814 en
heal.identifier.secondary 1512814 en
heal.publicationDate 2005 en
heal.abstract This work presents the results of the studies concerning the applications of the feedforward neural networks to the prediction of propagation path loss in urban and suburban environment. First, neural network models are designed in order to predict the path loss. Further investigations are made on an error correction model, based on the combination between a theoretical model and a neural network. The performances of the neural models are compared to the measured path loss values from the measurements conducted in the city of Kavala and in Oia village on Santorini Island, Greece, based on the absolute mean square error, standard deviation and root mean square error between predicted and measured values. Also, the neural networks models are compared to each other and to the COST 231-Walfisch-Ikegami. © 2005 IEEE. en
heal.journalName 2005 IEEE International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob'2005 en
dc.identifier.doi 10.1109/WIMOB.2005.1512814 en
dc.identifier.volume 1 en
dc.identifier.spage 44 en
dc.identifier.epage 49 en


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