1 Ocak 2016 Konferans bildirisi Açık Erişim

Yesilkaya, Anil; Karatalay, Onur; Ogrenci, Arif Selcuk; Panayirci, Erdal

DataCite XML

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  <identifier identifierType="URL">https://aperta.ulakbim.gov.tr/record/111782</identifier>
  <creators>
    <creator>
      <creatorName>Yesilkaya, Anil</creatorName>
      <givenName>Anil</givenName>
      <familyName>Yesilkaya</familyName>
      <affiliation>Kadir Has Univ, Istanbul, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Karatalay, Onur</creatorName>
      <givenName>Onur</givenName>
      <familyName>Karatalay</familyName>
      <affiliation>Kadir Has Univ, Istanbul, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Ogrenci, Arif Selcuk</creatorName>
      <givenName>Arif Selcuk</givenName>
      <familyName>Ogrenci</familyName>
      <affiliation>Kadir Has Univ, Istanbul, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Panayirci, Erdal</creatorName>
      <givenName>Erdal</givenName>
      <familyName>Panayirci</familyName>
      <affiliation>Kadir Has Univ, Istanbul, Turkey</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Channel Estimation For Visible Light Communications Using Neural Networks</title>
  </titles>
  <publisher>Aperta</publisher>
  <publicationYear>2016</publicationYear>
  <dates>
    <date dateType="Issued">2016-01-01</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://aperta.ulakbim.gov.tr/record/111782</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.81043/aperta.111781</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.81043/aperta.111782</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="http://www.opendefinition.org/licenses/cc-by">Creative Commons Attribution</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="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 rain 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.</description>
  </descriptions>
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