Analysis of the Refined Mean-Field Approximation for the 802.11 Protocol Model
dc.contributor.author | Ispizua, Begoña | |
dc.contributor.author | Doncel Vicente, Josu | |
dc.date.accessioned | 2022-11-25T13:06:59Z | |
dc.date.available | 2022-11-25T13:06:59Z | |
dc.date.issued | 2022-11-12 | |
dc.identifier.citation | Sensors 22(22) : (2022) // Article ID 8754 | es_ES |
dc.identifier.issn | 1424-8220 | |
dc.identifier.uri | http://hdl.handle.net/10810/58549 | |
dc.description.abstract | Mean-field approximation is a method to investigate the behavior of stochastic models formed by a large number of interacting objects. A new approximation was recently established, i.e., the refined mean-field approximation, and its high accuracy when the number of objects is small has been shown. In this work, we consider the model of the 802.11 protocol, which is a discrete-time model and show how the refined mean-field approximation can be adapted to this model. Our results confirm the accuracy of the refined mean-field approximation when the model with N objects is in discrete time. | es_ES |
dc.description.sponsorship | This research was founded by the Department of Education of the Basque Government, Spain, through the Consolidated Research Group MATHMODE (IT1456-22) and by the Marie Sklodowska-Curie, grant agreement number 777778. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | MDPI | es_ES |
dc.relation | info:eu-repo/grantAgreement/EC/H2020/777778 | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
dc.subject | refined mean-field approximation | es_ES |
dc.subject | 802.11 protocol model | es_ES |
dc.title | Analysis of the Refined Mean-Field Approximation for the 802.11 Protocol Model | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.date.updated | 2022-11-24T14:43:49Z | |
dc.rights.holder | © 2022 by the authors.Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/ 4.0/). | es_ES |
dc.relation.publisherversion | https://www.mdpi.com/1424-8220/22/22/8754 | es_ES |
dc.identifier.doi | 10.3390/s22228754 | |
dc.contributor.funder | European Commission | |
dc.departamentoes | Matemáticas | |
dc.departamentoeu | Matematika |
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Except where otherwise noted, this item's license is described as © 2022 by the authors.Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/ 4.0/).