Channel estimation for visible light communications using neural networks
| gdc.relation.journal | 2016 International Joint Conference on Neural Networks (IJCNN) | en_US |
| dc.contributor.author | Yeşilkaya, Anıl | |
| dc.contributor.author | Karatalay, Onur | |
| dc.contributor.author | Öğrenci, Arif Selçuk | |
| dc.contributor.author | Panayırcı, Erdal | |
| dc.contributor.other | Electrical-Electronics Engineering | |
| dc.contributor.other | 05. Faculty of Engineering and Natural Sciences | |
| dc.contributor.other | 01. Kadir Has University | |
| dc.date.accessioned | 2019-06-28T11:10:44Z | |
| dc.date.available | 2019-06-28T11:10:44Z | |
| dc.date.issued | 2016 | |
| dc.description.abstract | Visible 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.citationcount | 16 | |
| dc.identifier.doi | 10.1109/IJCNN.2016.7727215 | en_US |
| dc.identifier.isbn | 9781509006199 | |
| dc.identifier.scopus | 2-s2.0-85007158483 | en_US |
| dc.identifier.uri | https://hdl.handle.net/20.500.12469/1275 | |
| dc.language.iso | en | en_US |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_US |
| dc.relation.ispartof | 2016 International Joint Conference on Neural Networks (IJCNN) | |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.title | Channel estimation for visible light communications using neural networks | en_US |
| dc.type | Conference Object | en_US |
| dspace.entity.type | Publication | |
| gdc.author.institutional | Öğrenci, Arif Selçuk | en_US |
| gdc.author.institutional | Panayırcı, Erdal | |
| gdc.bip.impulseclass | C4 | |
| gdc.bip.influenceclass | C4 | |
| gdc.bip.popularityclass | C4 | |
| gdc.coar.access | open access | |
| gdc.coar.type | text::conference output | |
| gdc.description.department | Fakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümü | en_US |
| gdc.description.endpage | 325 | |
| gdc.description.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
| gdc.description.startpage | 320 | en_US |
| gdc.description.volume | 2016-October | en_US |
| gdc.identifier.openalex | W3099816092 | |
| gdc.identifier.wos | WOS:000399925500043 | en_US |
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| gdc.oaire.keywords | Signal Processing (eess.SP) | |
| gdc.oaire.keywords | FOS: Computer and information sciences | |
| gdc.oaire.keywords | N/A | |
| gdc.oaire.keywords | Computer Science - Information Theory | |
| gdc.oaire.keywords | Information Theory (cs.IT) | |
| gdc.oaire.keywords | FOS: Electrical engineering, electronic engineering, information engineering | |
| gdc.oaire.keywords | Computer Science - Neural and Evolutionary Computing | |
| gdc.oaire.keywords | Neural and Evolutionary Computing (cs.NE) | |
| gdc.oaire.keywords | Electrical Engineering and Systems Science - Signal Processing | |
| gdc.oaire.popularity | 1.05594E-8 | |
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| gdc.oaire.sciencefields | 0202 electrical engineering, electronic engineering, information engineering | |
| gdc.oaire.sciencefields | 02 engineering and technology | |
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| gdc.openalex.toppercent | TOP 10% | |
| gdc.opencitations.count | 17 | |
| gdc.plumx.crossrefcites | 1 | |
| gdc.plumx.mendeley | 33 | |
| gdc.plumx.scopuscites | 25 | |
| gdc.scopus.citedcount | 25 | |
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