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<ArticleSet>
  <ARTICLE>
    <Journal>
      <PublisherName>مرکز منطقه ای اطلاع رسانی علوم و فناوری</PublisherName>
      <JournalTitle>Journal of Information Systems and Telecommunication (JIST) </JournalTitle>
      <ISSN>2322-1437</ISSN>
      <Volume>13</Volume>
      <Issue>52</Issue>
      <PubDate PubStatus="epublish">
        <Year>2026</Year>
        <Month>2</Month>
        <Day>3</Day>
      </PubDate>
    </Journal>
    <ArticleTitle>Optimally DBS Placement In 6G Communication Networks Using Improved Gray Wolf Optimization Algorithm to Enhance Network Energy Efficiency</ArticleTitle>
    <VernacularTitle>Optimally DBS Placement In 6G Communication Networks Using Improved Gray Wolf Optimization Algorithm to Enhance Network Energy Efficiency</VernacularTitle>
    <FirstPage>316</FirstPage>
    <LastPage>325</LastPage>
    <ELocationID EIdType="doi">10.66224/jist.50264.13.52.316</ELocationID>
    <Language>en</Language>
    <AuthorList>
      <Author>
        <FirstName>Hussein</FirstName>
        <LastName>Shakir Diwan Al-Khulaifawi</LastName>
        <Affiliation>Faculty of Electrical and Computer Engineering, University of Tabriz</Affiliation>
      </Author>
      <Author>
        <FirstName>Mahdi</FirstName>
        <LastName>Nangir</LastName>
        <Affiliation>University of Tabriz</Affiliation>
      </Author>
    </AuthorList>
    <History PubStatus="received">
      <Year>2025</Year>
      <Month>5</Month>
      <Day>17</Day>
    </History>
    <Abstract>&lt;p class="KeywordsHeader"&gt;&lt;span style="font-weight: normal; font-style: normal;"&gt;The transition to sixth-generation (6G) networks demands highly energy-efficient solutions for large-scale IoT services. Drone Base Stations (DBSs) offer flexible coverage, but their three-dimensional placement must be optimized to reduce both transmission and hovering energy. This paper, model DBS deployment as a power-minimization problem and introduce an Improved Grey Wolf Optimization (IGWO) algorithm that integrates adaptive control parameters, exponential weighting of leader contributions (alpha/beta/delta), and a dynamic control structure that progressively favors elite solutions. This design improves search efficiency in high-dimensional, nonlinear spaces and reduces the risk of premature convergence. Extensive MATLAB simulations across multiple propagation environments demonstrate that IGWO achieves lower network power consumption and faster convergence compared to standard metaheuristics, while preserving coverage and connectivity. Specifically, the simulation results demonstrate that the proposed method achieves a remarkable superiority over other optimization algorithms, showing more than a 2% improvement compared to the best among them the standard GWO algorithm&amp;mdash;thereby confirming its effectiveness and efficiency in low-power network scenarios.&lt;/span&gt;&lt;/p&gt;</Abstract>
    <ObjectList>
      <Object Type="Keyword">
        <Param Name="Value">6G communication networks</Param>
      </Object>
      <Object Type="Keyword">
        <Param Name="Value">Drone Base Stations (DBSs)</Param>
      </Object>
      <Object Type="Keyword">
        <Param Name="Value">Internet of Things (IoT)</Param>
      </Object>
      <Object Type="Keyword">
        <Param Name="Value">Improved Gray Wolf Optimization (IGWO)</Param>
      </Object>
      <Object Type="Keyword">
        <Param Name="Value">Energy Efficiency</Param>
      </Object>
    </ObjectList>
    <ArchiveCopySource DocType="Pdf">http://jist.ir/fa/Article/Download/50264</ArchiveCopySource>
  </ARTICLE>
</ArticleSet>