Channel estimation for visible light communications using neural networks

dc.contributor.authorYeşilkaya, Anıl
dc.contributor.authorKaratalay, Onur
dc.contributor.authorÖğrenci, Arif Selçuk
dc.contributor.authorPanayırcı, Erdal
dc.date.accessioned2019-06-28T11:10:44Z
dc.date.available2019-06-28T11:10:44Z
dc.date.issued2016
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.description.abstractVisible light communications (VLC) is an emerging field in technology and research. Estimating the channel taps is a major requirement for designing reliable communication systems. Due to the nonlinear characteristics of the VLC channel those parameters cannot be derived easily. They can be calculated by means of software simulation. In this work a novel methodology is proposed for the prediction of channel parameters using neural networks. Measurements conducted in a controlled experimental setup are used to train neural networks for channel tap prediction. Our experiment results indicate that neural networks can be effectively trained to predict channel taps under different environmental conditions. © 2016 IEEE.en_US]
dc.identifier.citation16
dc.identifier.doi10.1109/IJCNN.2016.7727215en_US
dc.identifier.endpage325
dc.identifier.isbn9781509006199
dc.identifier.scopus2-s2.0-85007158483en_US
dc.identifier.startpage320en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12469/1275
dc.identifier.volume2016-Octoberen_US
dc.identifier.wosWOS:000399925500043en_US
dc.institutionauthorÖğrenci, Arif Selçuken_US
dc.institutionauthorPanayırcı, Erdal
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.journal2016 International Joint Conference on Neural Networks (IJCNN)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.titleChannel estimation for visible light communications using neural networksen_US
dc.typeConference Objecten_US
dspace.entity.typePublication
relation.isAuthorOfPublication5371ab5d-9cd9-4d1f-8681-a65b3d5d6add
relation.isAuthorOfPublication.latestForDiscovery5371ab5d-9cd9-4d1f-8681-a65b3d5d6add

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