Artificial Intelligence Techniques Evaluation for Modulation Format Identification in Optical Networks
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Journal of Microwaves. Optoelectronics and Electromagnetic Applications |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2179-10742021000400689 |
Resumo: | Abstract In an elastic network paradigm, where the transceiver is able to control several characteristics of the transmitted signal according to the optical link quality and capacity demand, receivers able to automatically detect the modulation format are fundamental to recover the transmitted signal without the necessity of headers that reduce system capacity. This work presents a simulated performance comparison of six methods for blind identification of modulation format in high-capacity optical systems: k-nearest neighbors (KNN), k-means, fuzzy c-means, deep neural networks, support-vector machine (SVM) and peak-to-average power ratio (PAPR). The transmitted channels were 64-GBd modulated with the following modulation formats available at the transceiver: QPSK, 16QAM, 64QAM, and 256QAM. The optical link was emulated considering several impairments, as amplified spontaneous emission from optical amplifiers, frequency and phase noise from lasers, and polarization rotation and differential group delay from the propagation. The support-vector machine algorithm presented the most robust results. |
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Journal of Microwaves. Optoelectronics and Electromagnetic Applications |
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Artificial Intelligence Techniques Evaluation for Modulation Format Identification in Optical Networksdigital signal processingelastic optical networksmodulation format blind identificationAbstract In an elastic network paradigm, where the transceiver is able to control several characteristics of the transmitted signal according to the optical link quality and capacity demand, receivers able to automatically detect the modulation format are fundamental to recover the transmitted signal without the necessity of headers that reduce system capacity. This work presents a simulated performance comparison of six methods for blind identification of modulation format in high-capacity optical systems: k-nearest neighbors (KNN), k-means, fuzzy c-means, deep neural networks, support-vector machine (SVM) and peak-to-average power ratio (PAPR). The transmitted channels were 64-GBd modulated with the following modulation formats available at the transceiver: QPSK, 16QAM, 64QAM, and 256QAM. The optical link was emulated considering several impairments, as amplified spontaneous emission from optical amplifiers, frequency and phase noise from lasers, and polarization rotation and differential group delay from the propagation. The support-vector machine algorithm presented the most robust results.Sociedade Brasileira de Microondas e Optoeletrônica e Sociedade Brasileira de Eletromagnetismo2021-12-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S2179-10742021000400689Journal of Microwaves, Optoelectronics and Electromagnetic Applications v.20 n.4 2021reponame:Journal of Microwaves. Optoelectronics and Electromagnetic Applicationsinstname:Sociedade Brasileira de Microondas e Optoeletrônica (SBMO)instacron:SBMO10.1590/2179-10742021v20i4254759info:eu-repo/semantics/openAccessSouza,André Luiz Nunes deSutili,TiagoCruz Júnior,José Hélio daFigueiredo,Rafael Carvalhoeng2021-11-11T00:00:00Zoai:scielo:S2179-10742021000400689Revistahttp://www.jmoe.org/index.php/jmoe/indexONGhttps://old.scielo.br/oai/scielo-oai.php||editor_jmoe@sbmo.org.br2179-10742179-1074opendoar:2021-11-11T00:00Journal of Microwaves. Optoelectronics and Electromagnetic Applications - Sociedade Brasileira de Microondas e Optoeletrônica (SBMO)false |
dc.title.none.fl_str_mv |
Artificial Intelligence Techniques Evaluation for Modulation Format Identification in Optical Networks |
title |
Artificial Intelligence Techniques Evaluation for Modulation Format Identification in Optical Networks |
spellingShingle |
Artificial Intelligence Techniques Evaluation for Modulation Format Identification in Optical Networks Souza,André Luiz Nunes de digital signal processing elastic optical networks modulation format blind identification |
title_short |
Artificial Intelligence Techniques Evaluation for Modulation Format Identification in Optical Networks |
title_full |
Artificial Intelligence Techniques Evaluation for Modulation Format Identification in Optical Networks |
title_fullStr |
Artificial Intelligence Techniques Evaluation for Modulation Format Identification in Optical Networks |
title_full_unstemmed |
Artificial Intelligence Techniques Evaluation for Modulation Format Identification in Optical Networks |
title_sort |
Artificial Intelligence Techniques Evaluation for Modulation Format Identification in Optical Networks |
author |
Souza,André Luiz Nunes de |
author_facet |
Souza,André Luiz Nunes de Sutili,Tiago Cruz Júnior,José Hélio da Figueiredo,Rafael Carvalho |
author_role |
author |
author2 |
Sutili,Tiago Cruz Júnior,José Hélio da Figueiredo,Rafael Carvalho |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Souza,André Luiz Nunes de Sutili,Tiago Cruz Júnior,José Hélio da Figueiredo,Rafael Carvalho |
dc.subject.por.fl_str_mv |
digital signal processing elastic optical networks modulation format blind identification |
topic |
digital signal processing elastic optical networks modulation format blind identification |
description |
Abstract In an elastic network paradigm, where the transceiver is able to control several characteristics of the transmitted signal according to the optical link quality and capacity demand, receivers able to automatically detect the modulation format are fundamental to recover the transmitted signal without the necessity of headers that reduce system capacity. This work presents a simulated performance comparison of six methods for blind identification of modulation format in high-capacity optical systems: k-nearest neighbors (KNN), k-means, fuzzy c-means, deep neural networks, support-vector machine (SVM) and peak-to-average power ratio (PAPR). The transmitted channels were 64-GBd modulated with the following modulation formats available at the transceiver: QPSK, 16QAM, 64QAM, and 256QAM. The optical link was emulated considering several impairments, as amplified spontaneous emission from optical amplifiers, frequency and phase noise from lasers, and polarization rotation and differential group delay from the propagation. The support-vector machine algorithm presented the most robust results. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-12-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2179-10742021000400689 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2179-10742021000400689 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/2179-10742021v20i4254759 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Sociedade Brasileira de Microondas e Optoeletrônica e Sociedade Brasileira de Eletromagnetismo |
publisher.none.fl_str_mv |
Sociedade Brasileira de Microondas e Optoeletrônica e Sociedade Brasileira de Eletromagnetismo |
dc.source.none.fl_str_mv |
Journal of Microwaves, Optoelectronics and Electromagnetic Applications v.20 n.4 2021 reponame:Journal of Microwaves. Optoelectronics and Electromagnetic Applications instname:Sociedade Brasileira de Microondas e Optoeletrônica (SBMO) instacron:SBMO |
instname_str |
Sociedade Brasileira de Microondas e Optoeletrônica (SBMO) |
instacron_str |
SBMO |
institution |
SBMO |
reponame_str |
Journal of Microwaves. Optoelectronics and Electromagnetic Applications |
collection |
Journal of Microwaves. Optoelectronics and Electromagnetic Applications |
repository.name.fl_str_mv |
Journal of Microwaves. Optoelectronics and Electromagnetic Applications - Sociedade Brasileira de Microondas e Optoeletrônica (SBMO) |
repository.mail.fl_str_mv |
||editor_jmoe@sbmo.org.br |
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1752122127052439552 |