Photonic neuromorphic technologies in optical communications

Machine learning (ML) and neuromorphic computing have been enforcing problem-solving in many applications. Such approaches found fertile ground in optical communications, a technological field that is very demanding in terms of computational speed and complexity. The latest breakthroughs are strongl...

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Author: Argyris, Apostolos
Format: article
Status:Published version
Publication Date:2022
Country:España
Institution:Consejo Superior de Investigaciones Científicas (CSIC)
Repository:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/271202
Online Access:http://hdl.handle.net/10261/271202
Access Level:Open access
Keyword:Fiber transmission
Machine learning
Neuromorphic computing
Optical communications
Photonic systems
Reservoir computing
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spelling Photonic neuromorphic technologies in optical communicationsArgyris, ApostolosFiber transmissionMachine learningNeuromorphic computingOptical communicationsPhotonic systemsReservoir computingMachine learning (ML) and neuromorphic computing have been enforcing problem-solving in many applications. Such approaches found fertile ground in optical communications, a technological field that is very demanding in terms of computational speed and complexity. The latest breakthroughs are strongly supported by advanced signal processing, implemented in the digital domain. Algorithms of different levels of complexity aim at improving data recovery, expanding the reach of transmission, validating the integrity of the optical network operation, and monitoring data transfer faults. Lately, the concept of reservoir computing (RC) inspired hardware implementations in photonics that may offer revolutionary solutions in this field. In a brief introduction, I discuss some of the established digital signal processing (DSP) techniques and some new approaches based on ML and neural network (NN) architectures. In the main part, I review the latest neuromorphic computing proposals that specifically apply to photonic hardware and give new perspectives on addressing signal processing in optical communications. I discuss the fundamental topologies in photonic feed-forward and recurrent network implementations. Finally, I review the photonic topologies that were initially tested for channel equalization benchmark tasks, and then in fiber transmission systems, for optical header recognition, data recovery, and modulation format identification.The author would like to acknowledge the support of the Severo Ochoa and Maria de Maeztu Program for Centers and Units of Excellence in R&D, grant MDM-2017-0711 (funded by MCIN/AEI/10.13039/501100011033), the European Union’s Horizon 2020 Future and Emerging Technologies program (Grant Agreement No. 899265, ADOPD), the European Union’s Horizon 2020 Marie-Skłodowska Curie Training Network program (Grand agreement No. 860360, POST-DIGITAL) and the Ministerio de Ciencia e Innovación through the project DECAPH (funded by PID2019-111537GB-C21/AEI/10.13039/501100011033).Walter de GruyterMinisterio de Ciencia e Innovación (España)Agencia Estatal de Investigación (España)European CommissionConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]2022202220222022info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_dcae04bcPublisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/271202reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/MICINN//MDM-2017-0711info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-111537GB-C21info:eu-repo/grantAgreement/EC/H2020/899265info:eu-repo/grantAgreement/EC/H2020/860360http://dx.doi.org/10.1515/nanoph-2021-0578Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/2712022026-05-22T06:33:51Z
dc.title.none.fl_str_mv Photonic neuromorphic technologies in optical communications
title Photonic neuromorphic technologies in optical communications
spellingShingle Photonic neuromorphic technologies in optical communications
Argyris, Apostolos
Fiber transmission
Machine learning
Neuromorphic computing
Optical communications
Photonic systems
Reservoir computing
title_short Photonic neuromorphic technologies in optical communications
title_full Photonic neuromorphic technologies in optical communications
title_fullStr Photonic neuromorphic technologies in optical communications
title_full_unstemmed Photonic neuromorphic technologies in optical communications
title_sort Photonic neuromorphic technologies in optical communications
dc.creator.none.fl_str_mv Argyris, Apostolos
author Argyris, Apostolos
author_facet Argyris, Apostolos
author_role author
dc.contributor.none.fl_str_mv Ministerio de Ciencia e Innovación (España)
Agencia Estatal de Investigación (España)
European Commission
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Fiber transmission
Machine learning
Neuromorphic computing
Optical communications
Photonic systems
Reservoir computing
topic Fiber transmission
Machine learning
Neuromorphic computing
Optical communications
Photonic systems
Reservoir computing
description Machine learning (ML) and neuromorphic computing have been enforcing problem-solving in many applications. Such approaches found fertile ground in optical communications, a technological field that is very demanding in terms of computational speed and complexity. The latest breakthroughs are strongly supported by advanced signal processing, implemented in the digital domain. Algorithms of different levels of complexity aim at improving data recovery, expanding the reach of transmission, validating the integrity of the optical network operation, and monitoring data transfer faults. Lately, the concept of reservoir computing (RC) inspired hardware implementations in photonics that may offer revolutionary solutions in this field. In a brief introduction, I discuss some of the established digital signal processing (DSP) techniques and some new approaches based on ML and neural network (NN) architectures. In the main part, I review the latest neuromorphic computing proposals that specifically apply to photonic hardware and give new perspectives on addressing signal processing in optical communications. I discuss the fundamental topologies in photonic feed-forward and recurrent network implementations. Finally, I review the photonic topologies that were initially tested for channel equalization benchmark tasks, and then in fiber transmission systems, for optical header recognition, data recovery, and modulation format identification.
publishDate 2022
dc.date.none.fl_str_mv 2022
2022
2022
2022
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_dcae04bc
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/271202
url http://hdl.handle.net/10261/271202
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
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info:eu-repo/grantAgreement/MICINN//MDM-2017-0711
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-111537GB-C21
info:eu-repo/grantAgreement/EC/H2020/899265
info:eu-repo/grantAgreement/EC/H2020/860360
http://dx.doi.org/10.1515/nanoph-2021-0578
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Walter de Gruyter
publisher.none.fl_str_mv Walter de Gruyter
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
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