Cloud-based implementation of a SON radio resources planning system for mobile networks and integration in SaaS metric

Detalhes bibliográficos
Autor(a) principal: Cortesão, R.
Data de Publicação: 2021
Outros Autores: Fernandes, D., Soares, G., Clemente, D., Sebastião, P., Ferreira, L. S.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Texto Completo: http://hdl.handle.net/10071/22937
Resumo: In mobile network deployments of growing size, the optimum and fast planning of radio resources are a key task. Cloud services enable efficient and scalable implementation of procedures and algorithms. In this paper, a proof of concept implementation of a cloud-based network planning work pattern using Amazon Web Services (AWS) is presented, containing new and efficient radio resource planning algorithms for 3G, 4G and 5G systems. It extracts configuration and performance data from the network, enabling to accurately estimate cells coverage, identify neighboring cells and optimally plan scrambling codes (SCs) and physical cell identity (PCI) in 3G and 4G/5G networks, respectively. This implementation was integrated and is available in the commercial Metric Software-as-a-Service (SaaS) monitoring and planning tool. The cloud-based planning system is demonstrated in various canonical and realistic Universal Mobile Telecommunications System (UMTS) and Long Term Evolution (LTE) scenarios, and compared to an algorithm previously used by Metric. For a small LTE realistic scenario consisting of 9 sites and 23 cells, it takes less than 0.6 seconds to perform the planning. For an UMTS realistic scenario with 12 484 unplanned cells, the planning is efficiently achieved, taking less than 8 seconds, and guaranteeing no collisions between first order neighboring cells. The proposed concept is proved, as this system, capable of automatically planning 3/4/5G realistic networks of multi-vendor equipment, makes Metric more attractive to the market.
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spelling Cloud-based implementation of a SON radio resources planning system for mobile networks and integration in SaaS metricCloud computingCoverage estimationProof-of-conceptOptimized planning toolMetric platformRadio resourcesSONCellular networksSaaS implementationEfficient algorithmsIn mobile network deployments of growing size, the optimum and fast planning of radio resources are a key task. Cloud services enable efficient and scalable implementation of procedures and algorithms. In this paper, a proof of concept implementation of a cloud-based network planning work pattern using Amazon Web Services (AWS) is presented, containing new and efficient radio resource planning algorithms for 3G, 4G and 5G systems. It extracts configuration and performance data from the network, enabling to accurately estimate cells coverage, identify neighboring cells and optimally plan scrambling codes (SCs) and physical cell identity (PCI) in 3G and 4G/5G networks, respectively. This implementation was integrated and is available in the commercial Metric Software-as-a-Service (SaaS) monitoring and planning tool. The cloud-based planning system is demonstrated in various canonical and realistic Universal Mobile Telecommunications System (UMTS) and Long Term Evolution (LTE) scenarios, and compared to an algorithm previously used by Metric. For a small LTE realistic scenario consisting of 9 sites and 23 cells, it takes less than 0.6 seconds to perform the planning. For an UMTS realistic scenario with 12 484 unplanned cells, the planning is efficiently achieved, taking less than 8 seconds, and guaranteeing no collisions between first order neighboring cells. The proposed concept is proved, as this system, capable of automatically planning 3/4/5G realistic networks of multi-vendor equipment, makes Metric more attractive to the market.IEEE2021-07-15T13:01:01Z2021-01-01T00:00:00Z20212021-07-15T14:00:12Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10071/22937eng2169-353610.1109/ACCESS.2021.3087398Cortesão, R.Fernandes, D.Soares, G.Clemente, D.Sebastião, P.Ferreira, L. S.info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-07-07T02:48:24Zoai:repositorio.iscte-iul.pt:10071/22937Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T18:07:48.736122Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Cloud-based implementation of a SON radio resources planning system for mobile networks and integration in SaaS metric
title Cloud-based implementation of a SON radio resources planning system for mobile networks and integration in SaaS metric
spellingShingle Cloud-based implementation of a SON radio resources planning system for mobile networks and integration in SaaS metric
Cortesão, R.
Cloud computing
Coverage estimation
Proof-of-concept
Optimized planning tool
Metric platform
Radio resources
SON
Cellular networks
SaaS implementation
Efficient algorithms
title_short Cloud-based implementation of a SON radio resources planning system for mobile networks and integration in SaaS metric
title_full Cloud-based implementation of a SON radio resources planning system for mobile networks and integration in SaaS metric
title_fullStr Cloud-based implementation of a SON radio resources planning system for mobile networks and integration in SaaS metric
title_full_unstemmed Cloud-based implementation of a SON radio resources planning system for mobile networks and integration in SaaS metric
title_sort Cloud-based implementation of a SON radio resources planning system for mobile networks and integration in SaaS metric
author Cortesão, R.
author_facet Cortesão, R.
Fernandes, D.
Soares, G.
Clemente, D.
Sebastião, P.
Ferreira, L. S.
author_role author
author2 Fernandes, D.
Soares, G.
Clemente, D.
Sebastião, P.
Ferreira, L. S.
author2_role author
author
author
author
author
dc.contributor.author.fl_str_mv Cortesão, R.
Fernandes, D.
Soares, G.
Clemente, D.
Sebastião, P.
Ferreira, L. S.
dc.subject.por.fl_str_mv Cloud computing
Coverage estimation
Proof-of-concept
Optimized planning tool
Metric platform
Radio resources
SON
Cellular networks
SaaS implementation
Efficient algorithms
topic Cloud computing
Coverage estimation
Proof-of-concept
Optimized planning tool
Metric platform
Radio resources
SON
Cellular networks
SaaS implementation
Efficient algorithms
description In mobile network deployments of growing size, the optimum and fast planning of radio resources are a key task. Cloud services enable efficient and scalable implementation of procedures and algorithms. In this paper, a proof of concept implementation of a cloud-based network planning work pattern using Amazon Web Services (AWS) is presented, containing new and efficient radio resource planning algorithms for 3G, 4G and 5G systems. It extracts configuration and performance data from the network, enabling to accurately estimate cells coverage, identify neighboring cells and optimally plan scrambling codes (SCs) and physical cell identity (PCI) in 3G and 4G/5G networks, respectively. This implementation was integrated and is available in the commercial Metric Software-as-a-Service (SaaS) monitoring and planning tool. The cloud-based planning system is demonstrated in various canonical and realistic Universal Mobile Telecommunications System (UMTS) and Long Term Evolution (LTE) scenarios, and compared to an algorithm previously used by Metric. For a small LTE realistic scenario consisting of 9 sites and 23 cells, it takes less than 0.6 seconds to perform the planning. For an UMTS realistic scenario with 12 484 unplanned cells, the planning is efficiently achieved, taking less than 8 seconds, and guaranteeing no collisions between first order neighboring cells. The proposed concept is proved, as this system, capable of automatically planning 3/4/5G realistic networks of multi-vendor equipment, makes Metric more attractive to the market.
publishDate 2021
dc.date.none.fl_str_mv 2021-07-15T13:01:01Z
2021-01-01T00:00:00Z
2021
2021-07-15T14:00:12Z
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/10071/22937
url http://hdl.handle.net/10071/22937
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2169-3536
10.1109/ACCESS.2021.3087398
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv IEEE
publisher.none.fl_str_mv IEEE
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
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institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
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