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

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dc.affiliation.dptoUC3M. Departamento de Ingeniería Telemáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Network Technologieses
dc.contributor.authorAyala Romero, José A.
dc.contributor.authorGarcía Saavedra, Andrés
dc.contributor.authorGramaglia, Marco
dc.contributor.authorBanchs Roca, Albert
dc.contributor.authorCosta-Pérez, Xavier
dc.contributor.authorAlcaraz, Juan J.
dc.contributor.funderEuropean Commissionen
dc.date.accessioned2022-07-27T12:14:55Z
dc.date.available2021-02-01T13:10:15Z
dc.date.issued2022-07-01
dc.description.abstractThe virtualization of radio access networks (vRAN) is the last milestone in the NFV revolution. However, the complexrelationship between computing and radio dynamics make vRAN resource control particularly daunting. We present vrAIn, a resourceorchestrator 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) algorithmbased 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 aproduction 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 ourknowledge, this is the first work that thoroughly studies the computational behavior of vRANs and the first approach to a model-freesolution that does not need to assume any particular platform or context.en
dc.description.sponsorshipThis work was partially supported by the European Commission through Grant No. 856709 (5Growth) and Grant No. 856950 (5G-TOURS); by Science Foundation Ireland (SFI) through Grant No. 17/CDA/4760; and AEI/FEDER through project AIM under Grant No. TEC2016-76465-C2-1-R. Furthermore, the work is closely related to the EU project DAEMON (Grant No. 101017109).en
dc.format.extent18
dc.identifier.bibliographicCitationAyala Romero, J.A., García Saavedra, A., Gramaglia, M., Costa Pérez, X., Banchs, A. y Alcaraz, J.J. vrAIn: Deep Learning based Orchestration for Computing and Radio Resources in vRANs. IEEE Transactions on Mobile Computing, 21(7), July 2022, pp. 2652-2670en
dc.identifier.doihttps://doi.org/10.1109/TMC.2020.3043100
dc.identifier.issn1536-1233
dc.identifier.publicationfirstpage2652
dc.identifier.publicationissue7
dc.identifier.publicationlastpage2670
dc.identifier.publicationtitleIEEE Transactions on Mobile Computingen
dc.identifier.publicationvolume21
dc.identifier.urihttps://hdl.handle.net/10016/31831
dc.identifier.uxxiAR/0000026395
dc.language.isoeng
dc.publisherIEEEen
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/856950
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/856709
dc.relation.projectIDGobierno de España. TEC2016-76465-C2-1-Res
dc.rights© 2020 IEEE.en
dc.rights.accessRightsopen accessen
dc.subject.ecienciaTelecomunicacioneses
dc.subject.otherRAN virtualizationen
dc.subject.otherResource managementen
dc.subject.otherMachine learningen
dc.titlevrAIn: Deep Learning based Orchestration for Computing and Radio Resources in vRANsen
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
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