A Comprehensive Survey on Machine and Deep Learning for Optical Communications
The increasing complexity of optical communication systems and networks necessitates advanced methodologies for extracting valuable insights from vast and heterogeneous datasets. Machine learning (ML) and deep learning (DL) have emerged as pivotal tools in this domain, revolutionizing data analysis...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11003870/ |
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| author | Mohammad Ali Amirabadi S. Alireza Nezamalhosseini Mohammad Hossein Kahaei Lawrence R. Chen |
| author_facet | Mohammad Ali Amirabadi S. Alireza Nezamalhosseini Mohammad Hossein Kahaei Lawrence R. Chen |
| author_sort | Mohammad Ali Amirabadi |
| collection | DOAJ |
| description | The increasing complexity of optical communication systems and networks necessitates advanced methodologies for extracting valuable insights from vast and heterogeneous datasets. Machine learning (ML) and deep learning (DL) have emerged as pivotal tools in this domain, revolutionizing data analysis and enabling automated self-configuration in optical communication systems. Their adoption is fueled by the growing intricacy of systems and links, driven by numerous adjustable and interdependent parameters. This complexity is particularly evident in areas such as coherent transceivers, advanced digital signal processing, optical performance monitoring, cross-layer network optimizations, and nonlinearity compensation. While ML and DL offer immense potential, their application in optical communications is still in its early stages, with significant opportunities remaining unexplored. Many algorithms have yet to be fully deployed in practical settings, underscoring the emerging nature of this research area. This paper presents a comprehensive survey of ML and DL applications across optical fiber communication (OFC), optical wireless communication (OWC), and optical communication networking (OCN), highlighting the challenges, current advancements, and future potential of these approaches. To address the identified gaps, this survey evaluates and compares ML and DL algorithms in terms of their performance, complexity, objectives, input data, metrics, and applications in optical communication. Specific emphasis is placed on identifying how these algorithms enhance system performance and optimization. Furthermore, the advantages and limitations of existing methods are analyzed, offering a clear perspective on the role of ML and DL in this domain. The survey also includes updated visual representations and domain-specific examples to elucidate the practical applications of ML and DL in OFC, OWC, and OCN. It concludes by discussing the open challenges, proposing potential solutions, and identifying promising future research directions to foster advancements in intelligent optical communication systems. |
| format | Article |
| id | doaj-art-1ecdc074d5974f588e0db4475de5e5d4 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-1ecdc074d5974f588e0db4475de5e5d42025-08-20T03:54:07ZengIEEEIEEE Access2169-35362025-01-0113887948884610.1109/ACCESS.2025.356955911003870A Comprehensive Survey on Machine and Deep Learning for Optical CommunicationsMohammad Ali Amirabadi0https://orcid.org/0000-0003-4190-3380S. Alireza Nezamalhosseini1https://orcid.org/0000-0001-9274-3300Mohammad Hossein Kahaei2https://orcid.org/0000-0002-1920-659XLawrence R. Chen3https://orcid.org/0000-0002-3760-4719Department of Electrical Engineering, Ferdowsi University of Mashhad, Mashhad, IranSchool of Electrical Engineering, Iran University of Science and Technology, Tehran, IranSchool of Electrical Engineering, Iran University of Science and Technology, Tehran, IranDepartment of Electrical and Computer Engineering, McGill University, Montreal, QC, CanadaThe increasing complexity of optical communication systems and networks necessitates advanced methodologies for extracting valuable insights from vast and heterogeneous datasets. Machine learning (ML) and deep learning (DL) have emerged as pivotal tools in this domain, revolutionizing data analysis and enabling automated self-configuration in optical communication systems. Their adoption is fueled by the growing intricacy of systems and links, driven by numerous adjustable and interdependent parameters. This complexity is particularly evident in areas such as coherent transceivers, advanced digital signal processing, optical performance monitoring, cross-layer network optimizations, and nonlinearity compensation. While ML and DL offer immense potential, their application in optical communications is still in its early stages, with significant opportunities remaining unexplored. Many algorithms have yet to be fully deployed in practical settings, underscoring the emerging nature of this research area. This paper presents a comprehensive survey of ML and DL applications across optical fiber communication (OFC), optical wireless communication (OWC), and optical communication networking (OCN), highlighting the challenges, current advancements, and future potential of these approaches. To address the identified gaps, this survey evaluates and compares ML and DL algorithms in terms of their performance, complexity, objectives, input data, metrics, and applications in optical communication. Specific emphasis is placed on identifying how these algorithms enhance system performance and optimization. Furthermore, the advantages and limitations of existing methods are analyzed, offering a clear perspective on the role of ML and DL in this domain. The survey also includes updated visual representations and domain-specific examples to elucidate the practical applications of ML and DL in OFC, OWC, and OCN. It concludes by discussing the open challenges, proposing potential solutions, and identifying promising future research directions to foster advancements in intelligent optical communication systems.https://ieeexplore.ieee.org/document/11003870/Machine learningdeep learningoptical fiber communicationoptical communication networkingoptical wireless communication |
| spellingShingle | Mohammad Ali Amirabadi S. Alireza Nezamalhosseini Mohammad Hossein Kahaei Lawrence R. Chen A Comprehensive Survey on Machine and Deep Learning for Optical Communications IEEE Access Machine learning deep learning optical fiber communication optical communication networking optical wireless communication |
| title | A Comprehensive Survey on Machine and Deep Learning for Optical Communications |
| title_full | A Comprehensive Survey on Machine and Deep Learning for Optical Communications |
| title_fullStr | A Comprehensive Survey on Machine and Deep Learning for Optical Communications |
| title_full_unstemmed | A Comprehensive Survey on Machine and Deep Learning for Optical Communications |
| title_short | A Comprehensive Survey on Machine and Deep Learning for Optical Communications |
| title_sort | comprehensive survey on machine and deep learning for optical communications |
| topic | Machine learning deep learning optical fiber communication optical communication networking optical wireless communication |
| url | https://ieeexplore.ieee.org/document/11003870/ |
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