Journal article Open Access

Elastic Slice-Aware Radio Resource Management with AI-Traffic Prediction

Khatibi, Sina; Jano, Alba

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  <identifier identifierType="DOI">10.5281/zenodo.3268510</identifier>
      <creatorName>Khatibi, Sina</creatorName>
      <affiliation>Nomor Research GmbH, Munich, Germany</affiliation>
      <creatorName>Jano, Alba</creatorName>
      <affiliation>Nomor Research GmbH, Munich, Germany</affiliation>
    <title>Elastic Slice-Aware Radio Resource Management with AI-Traffic Prediction</title>
    <date dateType="Issued">2019-06-21</date>
  <resourceType resourceTypeGeneral="JournalArticle"/>
    <alternateIdentifier alternateIdentifierType="url"></alternateIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.3268509</relatedIdentifier>
    <rights rightsURI="">Creative Commons Attribution Non Commercial No Derivatives 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
    <description descriptionType="Abstract">&lt;p&gt;Network virtualisation and network slicing are the two essential innovations in the next generation of mobile networks also known as the 5G networks. Based on these innovations, multiple network slices with different requirements and objectives can share the same physical infrastructure. The techniques to efficiently allocate the available radio resources to different slices based on their requirements and their priority, also known as inter-slice radio resource management, has been the subject of many studies. The formerly proposed algorithms either assume the slices request maximum contracted data rates or they react passively as the demands arrive. This paper proposes to use Artificial Intelligence (AI) approaches to learn the pattern of the traffic demand of each network slices and predict the demands in the next decision interval. Based on the prediction of the slices&amp;#39; demands, a novel model for elastic inter-slice radio resource management is proposed to increase the multiplexing gain while not compromising the quality of offered connectivity services to the slices. The proposed model is evaluated using a practical scenario. The numeric results show that while the performance of the model under full demand is similar to former models, its elastic resource management enables more efficient resource allocation when the traffic demands vary over time.&lt;/p&gt;</description>
    <description descriptionType="Other">© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.</description>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/100010661</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/761445/">761445</awardNumber>
      <awardTitle>5G Mobile Network Architecture for diverse services, use cases, and applications in 5G and beyond</awardTitle>
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