Modeling by optimal Artificial Neural Networks the prediction of propagation path loss in urban environments

S. P. Sotiroudis, S. K. Goudos, K. A. Gotsis, K. Siakavara, J. N. Sahalos

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper we present an alternative procedure for the prediction of propagation path loss in urban environments, which is based on Artificial Neural Networks (ANN). The goal of this work is to synthesize and model ANNs which would require entering at the input nodes a detailed and the same time small amount of information about the propagation environment. We apply the Differential Evolution (DE) algorithm, in conjunction with the Levenberg-Marquardt backpropagation algorithm in order to train different ANNs. The combined DE-LM method achieves better convergence of neural network weight optimization. We present two different ANN design cases with different number of input nodes. The general performance of the both ANNs shows their effectiveness to yield results with satisfactory accuracy in short time. The received results are compared to the respective ones yielded by the Ray-Tracing model and exhibit satisfactory accuracy.

Original languageEnglish
Title of host publicationProceedings of the 2013 IEEE-APS Topical Conference on Antennas and Propagation in Wireless Communications, IEEE APWC 2013
PublisherIEEE Computer Society
Pages599-602
Number of pages4
ISBN (Print)9781467356893
DOIs
Publication statusPublished - 2013
Event2013 3rd IEEE-APS Topical Conference on Antennas and Propagation in Wireless Communications, IEEE APWC 2013 - Turin, Italy
Duration: 9 Sept 201313 Sept 2013

Other

Other2013 3rd IEEE-APS Topical Conference on Antennas and Propagation in Wireless Communications, IEEE APWC 2013
Country/TerritoryItaly
CityTurin
Period9/09/1313/09/13

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