An RL approach to radio resource management in heterogeneous virtual RANs

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dc.contributor.author Tripathi, Sharda
dc.contributor.author Puligheddu, Corrado
dc.contributor.author Chiasserini, Carla Fabiana
dc.date.accessioned 2022-02-14T11:56:39Z
dc.date.available 2022-02-14T11:56:39Z
dc.date.issued 2021-03-09
dc.identifier.bibliographicCitation Tripathi, S., Puligheddu, C. & Chiasserini, C. F. (9-11 March 2021). An RL approach to radio resource management in heterogeneous virtual RANs [proceedings]. 2021 16th Annual Conference on Wireless On-demand Network Systems and Services Conference (WONS), Klosters, Switzerland.
dc.identifier.isbn 978-3-903176-35-5 (Electronic)
dc.identifier.isbn 978-1-6654-4659-4 (Print on Demand(PoD))
dc.identifier.uri http://hdl.handle.net/10016/34114
dc.description Proceedings of: 16th Annual Conference on Wireless On-demand Network Systems and Services Conference (WONS), 9-11 March 2021, Klosters, Switzerland.
dc.description.abstract 5G networks are primarily designed to support a wide range of services characterized by diverse key performance indicators (KPIs). A fundamental component of 5G networks, and a pivotal factor to the fulfillment of the services KPIs, is the virtual radio access network (RAN), which allows high flexibility on the control of the radio link. However, to fully exploit the potentiality of virtual RANs in non-stationary environments, an efficient mapping of the rapidly varying context to radio control decisions is not only essential, but also challenging owing to the non-trivial interdependence of network and channel conditions. In this paper, we propose CAREM, an RL framework for dynamic radio resource allocation, which selects the best link and modulation and coding scheme (MCS) for packet transmission, so as to meet the KPI requirements in heterogeneous virtual RANs. To show its effectiveness in real-world conditions, we provide a proof-of-concept through actual testbed implementation. Experimental results demonstrate that CAREM enables an efficient radio resource allocation, for any of the considered time periodicity of the decision-making process.
dc.description.sponsorship This work has been supported by the EC H2020 5GPPP 5GROWTH project (Grant No. 856709.)
dc.format.extent 8
dc.language.iso eng
dc.publisher IEEE
dc.rights © 2021, IEEE.
dc.subject.other 5G technology
dc.subject.other Reinforcement learning
dc.subject.other Virtual RAN
dc.subject.other Radio resource allocation
dc.subject.other Heterogeneous networks
dc.title An RL approach to radio resource management in heterogeneous virtual RANs
dc.type conferenceObject
dc.subject.eciencia Telecomunicaciones
dc.identifier.doi https://doi.org/10.23919/WONS51326.2021.9415591
dc.rights.accessRights openAccess
dc.relation.projectID info:eu-repo/grantAgreement/EC/856709
dc.type.version acceptedVersion
dc.relation.eventdate 2021-03-09
dc.relation.eventplace Klosters
dc.relation.eventtype proceeding
dc.identifier.publicationfirstpage 1
dc.identifier.publicationlastpage 8
dc.identifier.publicationtitle 2021 16th Annual Conference on Wireless On-demand Network Systems and Services Conference (WONS)
dc.contributor.funder European Commission
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