Repository logo
 
Publication

Improving mmWave backhaul reliability: a machine-learning based approach

2023-03-01Journal article Restricted access
Simple item page
dc.contributor.authorFerreira, Tânia
dc.contributor.authorFigueiredo, Alexandre
dc.contributor.authorRaposo, Duarte
dc.contributor.authorLuís, Miguel
dc.contributor.authorRito, Pedro
dc.contributor.authorSargento, Susana
dc.date.accessioned2023-05-18T11:24:15Z
dc.date.available2023-05-18T11:24:15Z
dc.date.issued2023-03-01
dc.description.abstractWiGig technologies, such as IEEE 802.11ad and later IEEE 802.11ay, provide multi-gigabit short-range communication at 60 GHz for bandwidth-intensive applications. However, this band suffers from high propagation losses that can only be compensated using highly directional antennas, making millimeter-wave (mmWave) links susceptible to blockage and errors. This high sensitivity to blockage leads to unstable and unreliable connections, since proprietary IEEE 802.11ad mechanisms, such as beamforming training, have high overhead, and can only be triggered when performance degradation is already detected, which compromises QoS and QoE even more.This article proposes a proactive machine learning framework that uses real-life data acquired in an outdoor setting to improve the reliability and resilience of a blockage-prone WiGig-based network. In particular, we propose a link quality classifier, which can differentiate between normal, long-term blockage and short-term operation with a test F1-score of 97%. Moreover, we introduce a novel deep learning forecasting model that can accurately capture the interactions between past multi-layer observations under different environments to produce accurate forecasts for 16 KPIs.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationFERREIRA, Tânia; [et al] – Improving mmWave backhaul reliability: A machine-learning based approach. Ad Hoc Networks. ISSN 1570-8705. Vol. 140 (2023), pp. 1-14.pt_PT
dc.identifier.doi10.1016/j.adhoc.2022.103050pt_PT
dc.identifier.eissn1570-8713
dc.identifier.issn1570-8705
dc.identifier.urihttp://hdl.handle.net/10400.21/16075
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherElsevierpt_PT
dc.relationCENTRO-01-0247-FEDER-045929 - FCTpt_PT
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S1570870522002220?via%3Dihubpt_PT
dc.subjectmmWave communicationspt_PT
dc.subjectNetwork reliabilitypt_PT
dc.subjectLink quality classificationpt_PT
dc.subjectLink quality predictionpt_PT
dc.subjectKPI forecastingpt_PT
dc.titleImproving mmWave backhaul reliability: a machine-learning based approachpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage14pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.titleAd Hoc Networkspt_PT
oaire.citation.volume140pt_PT
person.familyNameLuís
person.familyNameVIEIRA RITO
person.familyNameSargento
person.givenNameMiguel
person.givenNamePEDRO FILIPE
person.givenNameSusana
person.identifier.ciencia-id3418-A2F5-3CA4
person.identifier.ciencia-idB815-2F47-24FE
person.identifier.ciencia-id6C11-2B5E-FACB
person.identifier.orcid0000-0003-3488-2462
person.identifier.orcid0000-0002-1151-9268
person.identifier.orcid0000-0001-8761-8281
person.identifier.scopus-author-id36164286400
person.identifier.scopus-author-id56940657200
person.identifier.scopus-author-id6603312796
rcaap.rightsclosedAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication2eadcc1c-ff03-403a-9c42-f2e45f0fd528
relation.isAuthorOfPublication358358d2-b266-448e-bca6-6c5be202d10f
relation.isAuthorOfPublicatione0814076-e21c-4aab-beeb-0a06d3b01e24
relation.isAuthorOfPublication.latestForDiscovery2eadcc1c-ff03-403a-9c42-f2e45f0fd528

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
Improving_MLuis.pdf
Size:
3.06 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

ISEL - Eng. Elect. Tel. Comp. - Artigos