CNN-based eye pattern analysis and BER prediction in PAM4 inter-datacenter optical connections impaired by intercore crosstalk
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10071/24298 |
Resumo: | To meet the required future challenge of providing enough bandwidth to achieve high data traffic rates in datacenter links, four-level pulse amplitude modulation (PAM4) signals transmission in short-haul intensity modulation-direct detection datacenters connections supported by homogeneous weakly-coupled multicore fibers has been proposed. However, in such fibers, a physical effect known as inter-core crosstalk (ICXT) limits significantly the performance of short-reach connections by causing large bit error rate (BER) fluctuations that can lead undesirable system outages. In this work, a convolutional neural network (CNN) is proposed for eye-pattern analysis and BER prediction in PAM4 inter-datacenter optical connections impaired by ICXT, with the aim of optical performance monitoring. The performance of the CNN is assessed using the root mean square error (RMSE). Considering PAM4 interdatacenter links with one interfering core and for different skew-symbol rate products, extinction ratios and crosstalk levels, the results show that the implemented CNN is able to predict the BER without surpassing the RMSE limit. The CNNs trained with different optical parameters obtained the best performance in terms of generalization comparing to CNNs trained with specific optical parameters. These results confirm that the CNN-based models can be able to extract features from received eye patterns to predict the BER without prior knowledge of the transmitted signals. |
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CNN-based eye pattern analysis and BER prediction in PAM4 inter-datacenter optical connections impaired by intercore crosstalkBit error rateConvolutional neural networkInter-core crosstalkMachine learningMulticore fiberAprendizagem automáticaCrosstalk entre núcleosFibras multinúcleoRede neuronal convolucionalTaxa de erros bináriosTo meet the required future challenge of providing enough bandwidth to achieve high data traffic rates in datacenter links, four-level pulse amplitude modulation (PAM4) signals transmission in short-haul intensity modulation-direct detection datacenters connections supported by homogeneous weakly-coupled multicore fibers has been proposed. However, in such fibers, a physical effect known as inter-core crosstalk (ICXT) limits significantly the performance of short-reach connections by causing large bit error rate (BER) fluctuations that can lead undesirable system outages. In this work, a convolutional neural network (CNN) is proposed for eye-pattern analysis and BER prediction in PAM4 inter-datacenter optical connections impaired by ICXT, with the aim of optical performance monitoring. The performance of the CNN is assessed using the root mean square error (RMSE). Considering PAM4 interdatacenter links with one interfering core and for different skew-symbol rate products, extinction ratios and crosstalk levels, the results show that the implemented CNN is able to predict the BER without surpassing the RMSE limit. The CNNs trained with different optical parameters obtained the best performance in terms of generalization comparing to CNNs trained with specific optical parameters. These results confirm that the CNN-based models can be able to extract features from received eye patterns to predict the BER without prior knowledge of the transmitted signals.De modo a colmatar a necessidade de fornecer largura de banda suficiente para atingir altas taxas de tráfego de dados em ligações entre centros de dados, foi proposta a transmissão de sinais com modulação de impulsos em amplitude com 4 níveis (PAM4) em ligações de curto alcance entre centro de dados com modulação de intensidade e deteção direta suportadas por fibras homogéneas multinúcleo fracamente acopladas. No entanto, neste tipo de fibras, a diafonia entre núcleos (ICXT) limita significativamente o desempenho das ligações, causando grandes flutuações da taxa de erros binários (BER), o que pode conduzir à indisponibilidade da ligação. Neste trabalho, através da análise de diagramas de olho usando uma rede neuronal convolucional (CNN) é estimada a BER em ligações ópticas entre centros de dados PAM4 degradadas por ICXT com o objetivo de monitorização do desempenho. Para avaliar o desempenho da CNN é usada como métrica a raiz do erro quadrático médio (RMSE). Para diferentes atrasos de propagação entre núcleos, razões de extinção e níveis de diafonia, a CNN é capaz de prever BERs sem ultrapassar o limite estabelecido para o RMSE. As CNNs treinadas com diferentes parâmetros ópticos obtiveram o melhor desempenho em termos de generalização em comparação com CNNs treinadas com parâmetros ópticos específicos. Estes resultados confirmam que os modelos baseados em CNN são capazes de extrair informação a partir de imagens de diagramas de olhos, prevendo a BER sem conhecimento prévio dos sinais transmitidos.2022-01-25T11:20:25Z2021-12-20T00:00:00Z2021-12-202021-11info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10071/24298TID:202857352engEsteves, Sofia Pérsio Eugénioinfo: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:RCAAP2023-11-09T17:50:59Zoai:repositorio.iscte-iul.pt:10071/24298Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:25:13.939873Repositório 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 |
CNN-based eye pattern analysis and BER prediction in PAM4 inter-datacenter optical connections impaired by intercore crosstalk |
title |
CNN-based eye pattern analysis and BER prediction in PAM4 inter-datacenter optical connections impaired by intercore crosstalk |
spellingShingle |
CNN-based eye pattern analysis and BER prediction in PAM4 inter-datacenter optical connections impaired by intercore crosstalk Esteves, Sofia Pérsio Eugénio Bit error rate Convolutional neural network Inter-core crosstalk Machine learning Multicore fiber Aprendizagem automática Crosstalk entre núcleos Fibras multinúcleo Rede neuronal convolucional Taxa de erros binários |
title_short |
CNN-based eye pattern analysis and BER prediction in PAM4 inter-datacenter optical connections impaired by intercore crosstalk |
title_full |
CNN-based eye pattern analysis and BER prediction in PAM4 inter-datacenter optical connections impaired by intercore crosstalk |
title_fullStr |
CNN-based eye pattern analysis and BER prediction in PAM4 inter-datacenter optical connections impaired by intercore crosstalk |
title_full_unstemmed |
CNN-based eye pattern analysis and BER prediction in PAM4 inter-datacenter optical connections impaired by intercore crosstalk |
title_sort |
CNN-based eye pattern analysis and BER prediction in PAM4 inter-datacenter optical connections impaired by intercore crosstalk |
author |
Esteves, Sofia Pérsio Eugénio |
author_facet |
Esteves, Sofia Pérsio Eugénio |
author_role |
author |
dc.contributor.author.fl_str_mv |
Esteves, Sofia Pérsio Eugénio |
dc.subject.por.fl_str_mv |
Bit error rate Convolutional neural network Inter-core crosstalk Machine learning Multicore fiber Aprendizagem automática Crosstalk entre núcleos Fibras multinúcleo Rede neuronal convolucional Taxa de erros binários |
topic |
Bit error rate Convolutional neural network Inter-core crosstalk Machine learning Multicore fiber Aprendizagem automática Crosstalk entre núcleos Fibras multinúcleo Rede neuronal convolucional Taxa de erros binários |
description |
To meet the required future challenge of providing enough bandwidth to achieve high data traffic rates in datacenter links, four-level pulse amplitude modulation (PAM4) signals transmission in short-haul intensity modulation-direct detection datacenters connections supported by homogeneous weakly-coupled multicore fibers has been proposed. However, in such fibers, a physical effect known as inter-core crosstalk (ICXT) limits significantly the performance of short-reach connections by causing large bit error rate (BER) fluctuations that can lead undesirable system outages. In this work, a convolutional neural network (CNN) is proposed for eye-pattern analysis and BER prediction in PAM4 inter-datacenter optical connections impaired by ICXT, with the aim of optical performance monitoring. The performance of the CNN is assessed using the root mean square error (RMSE). Considering PAM4 interdatacenter links with one interfering core and for different skew-symbol rate products, extinction ratios and crosstalk levels, the results show that the implemented CNN is able to predict the BER without surpassing the RMSE limit. The CNNs trained with different optical parameters obtained the best performance in terms of generalization comparing to CNNs trained with specific optical parameters. These results confirm that the CNN-based models can be able to extract features from received eye patterns to predict the BER without prior knowledge of the transmitted signals. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-12-20T00:00:00Z 2021-12-20 2021-11 2022-01-25T11:20:25Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10071/24298 TID:202857352 |
url |
http://hdl.handle.net/10071/24298 |
identifier_str_mv |
TID:202857352 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
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.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 |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
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 |
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