Machine Learning in Short-Reach Optical Systems: A Comprehensive Survey Article Swipe
Chen Shao
,
Elias Giacoumidis
,
Syed Moktacim Billah
,
Shi Li
,
Jialei Li
,
Prashasti Sahu
,
André Richter
,
Michael Faerber
,
Tobias Kaefer
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/photonics11070613
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/photonics11070613
Recently, extensive research has been conducted to explore the utilization of machine learning (ML) algorithms in various direct-detected and (self)-coherent short-reach communication applications. These applications encompass a wide range of tasks, including bandwidth request prediction, signal quality monitoring, fault detection, traffic prediction, and digital signal processing (DSP)-based equalization. As a versatile approach, ML demonstrates the ability to address stochastic phenomena in optical systems networks where deterministic methods may fall short. However, when it comes to DSP equalization algorithms such as feed-forward/decision-feedback equalizers (FFEs/DFEs) and Volterra-based nonlinear equalizers, their performance improvements are often marginal, and their complexity is prohibitively high, especially in cost-sensitive short-reach communications scenarios such as passive optical networks (PONs). Time-series ML models offer distinct advantages over frequency-domain models in specific contexts. They excel in capturing temporal dependencies, handling irregular or nonlinear patterns effectively, and accommodating variable time intervals. Within this survey, we outline the application of ML techniques in short-reach communications, specifically emphasizing their utilization in high-bandwidth demanding PONs. We introduce a novel taxonomy for time-series methods employed in ML signal processing, providing a structured classification framework. Our taxonomy categorizes current time-series methods into four distinct groups: traditional methods, Fourier convolution-based methods, transformer-based models, and time-series convolutional networks. Finally, we highlight prospective research directions within this rapidly evolving field and outline specific solutions to mitigate the complexity associated with hardware implementations. We aim to pave the way for more practical and efficient deployment of ML approaches in short-reach optical communication systems by addressing complexity concerns.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/photonics11070613
- OA Status
- gold
- Cited By
- 4
- References
- 94
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4400235716
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4400235716Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/photonics11070613Digital Object Identifier
- Title
-
Machine Learning in Short-Reach Optical Systems: A Comprehensive SurveyWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-06-28Full publication date if available
- Authors
-
Chen Shao, Elias Giacoumidis, Syed Moktacim Billah, Shi Li, Jialei Li, Prashasti Sahu, André Richter, Michael Faerber, Tobias KaeferList of authors in order
- Landing page
-
https://doi.org/10.3390/photonics11070613Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.3390/photonics11070613Direct OA link when available
- Concepts
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Computer science, Digital signal processing, Signal processing, Bandwidth (computing), Computer engineering, Machine learning, Software deployment, Implementation, Artificial intelligence, Telecommunications, Computer hardware, Programming language, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
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4Total citation count in OpenAlex
- Citations by year (recent)
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2025: 4Per-year citation counts (last 5 years)
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94Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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