Journal article Open Access

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

Khatibi, Sina; Jano, Alba


MARC21 XML Export

<?xml version='1.0' encoding='UTF-8'?>
<record xmlns="http://www.loc.gov/MARC21/slim">
  <leader>00000nam##2200000uu#4500</leader>
  <controlfield tag="005">20200120154633.0</controlfield>
  <datafield tag="500" ind1=" " ind2=" ">
    <subfield code="a">© 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.</subfield>
  </datafield>
  <controlfield tag="001">3268510</controlfield>
  <datafield tag="711" ind1=" " ind2=" ">
    <subfield code="d">18-21 June 2019</subfield>
    <subfield code="g">EuCNC</subfield>
    <subfield code="a">European Conference on Networks and Communications</subfield>
    <subfield code="c">Valencia, Spain</subfield>
  </datafield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="u">Nomor Research GmbH, Munich, Germany</subfield>
    <subfield code="a">Jano, Alba</subfield>
  </datafield>
  <datafield tag="856" ind1="4" ind2=" ">
    <subfield code="s">763477</subfield>
    <subfield code="z">md5:807fa0fb9a478d44c07132b58e1d0a1e</subfield>
    <subfield code="u">https://zenodo.org/record/3268510/files/elastic.pdf</subfield>
  </datafield>
  <datafield tag="542" ind1=" " ind2=" ">
    <subfield code="l">open</subfield>
  </datafield>
  <datafield tag="260" ind1=" " ind2=" ">
    <subfield code="c">2019-06-21</subfield>
  </datafield>
  <datafield tag="909" ind1="C" ind2="O">
    <subfield code="p">openaire</subfield>
    <subfield code="o">oai:zenodo.org:3268510</subfield>
  </datafield>
  <datafield tag="100" ind1=" " ind2=" ">
    <subfield code="u">Nomor Research GmbH, Munich, Germany</subfield>
    <subfield code="a">Khatibi, Sina</subfield>
  </datafield>
  <datafield tag="245" ind1=" " ind2=" ">
    <subfield code="a">Elastic Slice-Aware Radio Resource Management with AI-Traffic Prediction</subfield>
  </datafield>
  <datafield tag="536" ind1=" " ind2=" ">
    <subfield code="c">761445</subfield>
    <subfield code="a">5G Mobile Network Architecture for diverse services, use cases, and applications in 5G and beyond</subfield>
  </datafield>
  <datafield tag="540" ind1=" " ind2=" ">
    <subfield code="u">https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode</subfield>
    <subfield code="a">Creative Commons Attribution Non Commercial No Derivatives 4.0 International</subfield>
  </datafield>
  <datafield tag="650" ind1="1" ind2="7">
    <subfield code="a">cc-by</subfield>
    <subfield code="2">opendefinition.org</subfield>
  </datafield>
  <datafield tag="520" ind1=" " ind2=" ">
    <subfield code="a">&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;</subfield>
  </datafield>
  <datafield tag="773" ind1=" " ind2=" ">
    <subfield code="n">doi</subfield>
    <subfield code="i">isVersionOf</subfield>
    <subfield code="a">10.5281/zenodo.3268509</subfield>
  </datafield>
  <datafield tag="024" ind1=" " ind2=" ">
    <subfield code="a">10.5281/zenodo.3268510</subfield>
    <subfield code="2">doi</subfield>
  </datafield>
  <datafield tag="980" ind1=" " ind2=" ">
    <subfield code="a">publication</subfield>
    <subfield code="b">article</subfield>
  </datafield>
</record>
46
39
views
downloads
All versions This version
Views 4648
Downloads 3939
Data volume 29.8 MB29.8 MB
Unique views 4446
Unique downloads 3939

Share

Cite as