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vrAIn: Deep Learning based Orchestration for Computing and Radio Resources in vRANs

Jose A. Ayala-Romero; Andres Garcia-Saavedra; Marco Gramaglia; Xavier Costa-Perez; Albert Banchs; Juan J. Alcaraz


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  <identifier identifierType="DOI">10.5281/zenodo.5037024</identifier>
  <creators>
    <creator>
      <creatorName>Jose A. Ayala-Romero</creatorName>
      <affiliation>Trinity College Dublin</affiliation>
    </creator>
    <creator>
      <creatorName>Andres Garcia-Saavedra</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-2005-2222</nameIdentifier>
      <affiliation>NEC Laboratories Europe GmbH</affiliation>
    </creator>
    <creator>
      <creatorName>Marco Gramaglia</creatorName>
      <affiliation>Universidad Carlos III de Madrid</affiliation>
    </creator>
    <creator>
      <creatorName>Xavier Costa-Perez</creatorName>
      <affiliation>NEC Laboratories Europe GmbH</affiliation>
    </creator>
    <creator>
      <creatorName>Albert Banchs</creatorName>
      <affiliation>Universidad Carlos III de Madrid &amp; IMDEA Networks</affiliation>
    </creator>
    <creator>
      <creatorName>Juan J. Alcaraz</creatorName>
      <affiliation>Universidad Politecnica de Cartagena</affiliation>
    </creator>
  </creators>
  <titles>
    <title>vrAIn: Deep Learning based Orchestration for Computing and Radio Resources in vRANs</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2021</publicationYear>
  <subjects>
    <subject>RAN virtualization</subject>
    <subject>resource management</subject>
    <subject>machine learning</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2021-01-15</date>
  </dates>
  <resourceType resourceTypeGeneral="JournalArticle"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/5037024</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsPreviousVersionOf" resourceTypeGeneral="JournalArticle">10.1109/TMC.2020.3043100</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.5037023</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/h2020daemon</relatedIdentifier>
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  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;The virtualization of radio access networks (vRAN) is the last milestone in the NFV revolution. However, the complex dependencies between computing and radio resources make vRAN resource control particularly daunting. We present vrAIn, a dynamic resource orchestrator for vRANs based on deep reinforcement learning. First, we use an autoencoder to project high-dimensional context data (traffic and channel quality patterns) into a latent representation. Then, we use a deep deterministic policy gradient (DDPG) algorithm based on an actor-critic neural network structure and a classifier to map contexts into resource control decisions. We have evaluated vrAIn experimentally, using an open-source LTE stack over different platforms, and via simulations over a production RAN. Our results show that: (i) vrAIn provides savings in computing capacity of up to 30% over CPU-agnostic methods; (ii) it improves the probability of meeting QoS targets by 25% over static policies; (iii) upon computing capacity under-provisioning, vrAIn improves throughput by 25% over state-of-the-art schemes; and (iv) it performs close to an optimal offline oracle. To our knowledge, this is the first work that thoroughly studies the computational behavior of vRANs and the first approach to a model-free solution that does not need to assume any particular platform or context.&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/100010661</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/101017109/">101017109</awardNumber>
      <awardTitle>Network intelligence for aDAptive and sElf-Learning MObile Networks</awardTitle>
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      <funderName>European Commission</funderName>
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      <awardTitle>5G-enabled Growth in Vertical Industries</awardTitle>
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    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/100010661</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/Horizon 2020 Framework Programme - Research and Innovation action/856950/">856950</awardNumber>
      <awardTitle>SmarT mObility, media and e-health for toURists and citizenS</awardTitle>
    </fundingReference>
  </fundingReferences>
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