1 Ocak 2021 Konferans bildirisi Açık Erişim

Alam, Muhammad Z.; Ates, Hasan F.; Baykas, Tuncer; Gunturk, Bahadir K.

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  <identifier identifierType="URL">https://aperta.ulakbim.gov.tr/record/239184</identifier>
  <creators>
    <creator>
      <creatorName>Alam, Muhammad Z.</creatorName>
      <givenName>Muhammad Z.</givenName>
      <familyName>Alam</familyName>
      <affiliation>Istanbul Medipol Univ, Sch Engn &amp; Nat Sci, Istanbul, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Ates, Hasan F.</creatorName>
      <givenName>Hasan F.</givenName>
      <familyName>Ates</familyName>
      <affiliation>Istanbul Medipol Univ, Sch Engn &amp; Nat Sci, Istanbul, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Baykas, Tuncer</creatorName>
      <givenName>Tuncer</givenName>
      <familyName>Baykas</familyName>
      <affiliation>Kadir Has Univ, Dept Elect Elect Engn, Istanbul, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Gunturk, Bahadir K.</creatorName>
      <givenName>Bahadir K.</givenName>
      <familyName>Gunturk</familyName>
      <affiliation>Istanbul Medipol Univ, Sch Engn &amp; Nat Sci, Istanbul, Turkey</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Analysis Of Deep Learning Based Path Loss Prediction From Satellite Images</title>
  </titles>
  <publisher>Aperta</publisher>
  <publicationYear>2021</publicationYear>
  <dates>
    <date dateType="Issued">2021-01-01</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://aperta.ulakbim.gov.tr/record/239184</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1109/SIU53274.2021.9478009</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">Determining the channel model parameters of a wireless communication system, either by measurements or by running electromagnetic propagation simulations, is a time-consuming process. Any rapid deployment of network demands faster determination of at least major channel parameters. In this paper, we investigate the idea of using deep convolutional neural networks and satellite images for channel parameters (i.e., path loss exponent n and shadowing factor sigma) prediction in a cellular network with aerial base stations. Specifically, we investigate the performance dependency of the method on three different factors: height of the transmitter antenna, quantization levels of the channel parameters and architectural design of CNN. The results presented in this paper show a high prediction accuracy of the channel parameters in real-time.</description>
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