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Performance Study and Enhancement of Access Barring for Massive Machine-Type Communications

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Performance Study and Enhancement of Access Barring for Massive Machine-Type Communications

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dc.contributor.author Vidal Catalá, José Ramón es_ES
dc.contributor.author Tello-Oquendo, Luis es_ES
dc.contributor.author Pla, Vicent es_ES
dc.contributor.author Guijarro, Luis es_ES
dc.date.accessioned 2020-12-08T04:31:51Z
dc.date.available 2020-12-08T04:31:51Z
dc.date.issued 2019-05-17 es_ES
dc.identifier.uri http://hdl.handle.net/10251/156560
dc.description.abstract [EN] Machine-type communications (MTC) is an emerging technology that boosts the development of the Internet of Things by providing ubiquitous connectivity and services. Cellular networks are an excellent choice for providing such hyper-connectivity thanks to their widely deployed infrastructure, among other features. However, dealing with a large number of connection requests is a primary challenge in the cellular-based MTC. Severe congestion episodes can occur when a large number of devices try to access the network almost simultaneously. Extended access barring (EAB) is a congestion control mechanism for the MTC that has been proposed by the 3GPP. In this paper, we carry out a thorough performance analysis of the EAB and show the limitations of its current specification. To overcome these limitations, we propose the two enhanced EAB schemes: the combined use of the EAB and access class barring, and the introduction of a congestion avoidance backoff after the barring status of a UE is switched to unbarred. It is shown through extensive simulations that our proposed solutions improve the key performance indicators. A high successful access probability can be achieved even in heavily congested scenarios, the access delay is shortened, and, most importantly, the number of required preamble retransmissions is reduced, which results in significant energy savings. Furthermore, we present an accurate congestion estimation method that solely relies on the information available at the base station. We show that this method permits a realistic and effective implementation of the EAB. es_ES
dc.description.sponsorship This work was supported in part by the Ministerio de Ciencia, Innovacion y Universidades (MCIU), Agencia Estatal de Investigacion (AEI) y Fondo Europeo de Desarrollo Regional (FEDER), UE, under Grant PGC2018-094151-B-I00, and in part by the ITACA Institute under Grant Ayudas ITACA 2019 es_ES
dc.language Inglés es_ES
dc.publisher Institute of Electrical and Electronics Engineers es_ES
dc.relation.ispartof IEEE Access es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Machine-to-machine communications es_ES
dc.subject Performance analysis es_ES
dc.subject Radio access networks es_ES
dc.subject 5G mobile communication es_ES
dc.subject.classification INGENIERIA TELEMATICA es_ES
dc.title Performance Study and Enhancement of Access Barring for Massive Machine-Type Communications es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1109/ACCESS.2019.2917618 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PGC2018-094151-B-I00/ES/SLICING DINAMICO EN REDES DE ACCESO RADIO 5G/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Comunicaciones - Departament de Comunicacions es_ES
dc.description.bibliographicCitation Vidal Catalá, JR.; Tello-Oquendo, L.; Pla, V.; Guijarro, L. (2019). Performance Study and Enhancement of Access Barring for Massive Machine-Type Communications. IEEE Access. 7:63745-63759. https://doi.org/10.1109/ACCESS.2019.2917618 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1109/ACCESS.2019.2917618 es_ES
dc.description.upvformatpinicio 63745 es_ES
dc.description.upvformatpfin 63759 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 7 es_ES
dc.identifier.eissn 2169-3536 es_ES
dc.relation.pasarela S\388587 es_ES
dc.contributor.funder Agencia Estatal de Investigación es_ES
dc.contributor.funder European Regional Development Fund es_ES


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