Improving mmWave backhaul reliability: a machine-learning based approach
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.21/16075 |
Resumo: | WiGig 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. |
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Improving mmWave backhaul reliability: a machine-learning based approachmmWave communicationsNetwork reliabilityLink quality classificationLink quality predictionKPI forecastingWiGig 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.ElsevierRCIPLFerreira, TâniaFigueiredo, AlexandreRaposo, DuarteLuís, MiguelRito, PedroSargento, Susana2023-05-18T11:24:15Z2023-03-012023-03-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.21/16075engFERREIRA, Tânia; [et al] – Improving mmWave backhaul reliability: A machine-learning based approach. Ad Hoc Networks. ISSN 1570-8705. Vol. 140 (2023), pp. 1-14.1570-870510.1016/j.adhoc.2022.1030501570-8713metadata only accessinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-08-03T10:14:21Zoai:repositorio.ipl.pt:10400.21/16075Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T20:23:40.660239Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Improving mmWave backhaul reliability: a machine-learning based approach |
title |
Improving mmWave backhaul reliability: a machine-learning based approach |
spellingShingle |
Improving mmWave backhaul reliability: a machine-learning based approach Ferreira, Tânia mmWave communications Network reliability Link quality classification Link quality prediction KPI forecasting |
title_short |
Improving mmWave backhaul reliability: a machine-learning based approach |
title_full |
Improving mmWave backhaul reliability: a machine-learning based approach |
title_fullStr |
Improving mmWave backhaul reliability: a machine-learning based approach |
title_full_unstemmed |
Improving mmWave backhaul reliability: a machine-learning based approach |
title_sort |
Improving mmWave backhaul reliability: a machine-learning based approach |
author |
Ferreira, Tânia |
author_facet |
Ferreira, Tânia Figueiredo, Alexandre Raposo, Duarte Luís, Miguel Rito, Pedro Sargento, Susana |
author_role |
author |
author2 |
Figueiredo, Alexandre Raposo, Duarte Luís, Miguel Rito, Pedro Sargento, Susana |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
RCIPL |
dc.contributor.author.fl_str_mv |
Ferreira, Tânia Figueiredo, Alexandre Raposo, Duarte Luís, Miguel Rito, Pedro Sargento, Susana |
dc.subject.por.fl_str_mv |
mmWave communications Network reliability Link quality classification Link quality prediction KPI forecasting |
topic |
mmWave communications Network reliability Link quality classification Link quality prediction KPI forecasting |
description |
WiGig 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. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-05-18T11:24:15Z 2023-03-01 2023-03-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.21/16075 |
url |
http://hdl.handle.net/10400.21/16075 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
FERREIRA, Tânia; [et al] – Improving mmWave backhaul reliability: A machine-learning based approach. Ad Hoc Networks. ISSN 1570-8705. Vol. 140 (2023), pp. 1-14. 1570-8705 10.1016/j.adhoc.2022.103050 1570-8713 |
dc.rights.driver.fl_str_mv |
metadata only access info:eu-repo/semantics/openAccess |
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metadata only access |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Elsevier |
publisher.none.fl_str_mv |
Elsevier |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799133509449678848 |