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

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dc.contributor.author Ayala Romero, José A.
dc.contributor.author García Saavedra, Andrés
dc.contributor.author Gramaglia, Marco
dc.contributor.author Banchs Roca, Albert
dc.contributor.author Costa-Pérez, Xavier
dc.contributor.author Alcaraz, Juan J.
dc.date.accessioned 2022-07-27T12:14:55Z
dc.date.available 2021-02-01T13:10:15Z
dc.date.issued 2022-07-01
dc.identifier.bibliographicCitation Ayala 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. 2652-2670
dc.identifier.issn 1536-1233
dc.identifier.uri http://hdl.handle.net/10016/31831
dc.description.abstract The 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.
dc.description.sponsorship This 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).
dc.format.extent 18
dc.language.iso eng
dc.publisher IEEE
dc.rights © 2020 IEEE.
dc.subject.other RAN virtualization
dc.subject.other Resource management
dc.subject.other Machine learning
dc.title vrAIn: Deep Learning based Orchestration for Computing and Radio Resources in vRANs
dc.type article
dc.subject.eciencia Telecomunicaciones
dc.identifier.doi https://doi.org/10.1109/TMC.2020.3043100
dc.rights.accessRights openAccess
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/856950
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/856709
dc.relation.projectID Gobierno de España. TEC2016-76465-C2-1-R
dc.type.version acceptedVersion
dc.identifier.publicationfirstpage 2652
dc.identifier.publicationissue 7
dc.identifier.publicationlastpage 2670
dc.identifier.publicationtitle IEEE Transactions on Mobile Computing
dc.identifier.publicationvolume 21
dc.identifier.uxxi AR/0000026395
dc.contributor.funder European Commission
dc.affiliation.dpto UC3M. Departamento de Ingeniería Telemática
dc.affiliation.grupoinv UC3M. Grupo de Investigación: Network Technologies
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