1 Ocak 2008 Konferans bildirisi Açık Erişim

Senol, Habib; Tepedelenlioglu, Cihan

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  <identifier identifierType="URL">https://aperta.ulakbim.gov.tr/record/38285</identifier>
  <creators>
    <creator>
      <creatorName>Senol, Habib</creatorName>
      <givenName>Habib</givenName>
      <familyName>Senol</familyName>
      <affiliation>Kadir Has Univ, Dept Comp Engn, Istanbul, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Tepedelenlioglu, Cihan</creatorName>
      <givenName>Cihan</givenName>
      <familyName>Tepedelenlioglu</familyName>
      <affiliation>Arizona State Univ, Dept Elect Engn, Tempe, AZ 85287 USA</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Distributed Estimation Over Parallel Fading Channels With Channel Estimation Error</title>
  </titles>
  <publisher>Aperta</publisher>
  <publicationYear>2008</publicationYear>
  <dates>
    <date dateType="Issued">2008-01-01</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://aperta.ulakbim.gov.tr/record/38285</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.81043/aperta.38284</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.81043/aperta.38285</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">We consider distributed estimation of a source observed by sensors in additive Gaussian noise, where the sensors are connected to a fusion center with unknown orthogonal (parallel) flat Rayleigh fading channels. We adopt a two-phase approach of (i) channel estimation with training, and (ii) source estimation given the channel estimates, where the total power is fixed. We prove that allocating half the total power into training is optimal, and show that compared to the perfect channel case, a performance loss of at least 6 dB is incurred. In addition, we show that unlike the perfect channel case, increasing the number of sensors will lead to an eventual degradation in performance. We characterize the optimum number of sensors as a function of the total power and noise statistics. Simulations corroborate our analytical findings.</description>
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