Harnessing machine learning for fiber-induced nonlinearity mitigation in long-haul coherent optical OFDM
Coherent optical orthogonal frequency division multiplexing (CO-OFDM) has attracted a lot of interest in optical fiber communications due to its simplified digital signal processing (DSP) units, high spectral-efficiency, flexibility, and tolerance to linear impairments. However, CO-OFDM’s...
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2018-12-01
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| Series: | Future Internet |
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| Online Access: | https://www.mdpi.com/1999-5903/11/1/2 |
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doaj-8ea53970ba34401bacbea27710f423d42020-11-25T01:41:04ZengMDPI AGFuture Internet1999-59032018-12-01111210.3390/fi11010002fi11010002Harnessing machine learning for fiber-induced nonlinearity mitigation in long-haul coherent optical OFDMElias Giacoumidis0Yi Lin1Jinlong Wei2Ivan Aldaya3Athanasios Tsokanos4Liam P. Barry5Radio and Optical Laboratory, School of Electronic Engineering, Dublin City University, Glasnevin 9, Dublin D09 Y5N0, IrelandRadio and Optical Laboratory, School of Electronic Engineering, Dublin City University, Glasnevin 9, Dublin D09 Y5N0, IrelandHuawei Technologies Düsseldorf GmbH, European Research Center, Riesstrasse 25, 80992 München, GermanyCampus São Joao da Boa Vista, State University of São Paulo (UNESP), 13876-750 São Paulo, BrazilCentre for Computer Science and Informatics Research, School of Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UKRadio and Optical Laboratory, School of Electronic Engineering, Dublin City University, Glasnevin 9, Dublin D09 Y5N0, IrelandCoherent optical orthogonal frequency division multiplexing (CO-OFDM) has attracted a lot of interest in optical fiber communications due to its simplified digital signal processing (DSP) units, high spectral-efficiency, flexibility, and tolerance to linear impairments. However, CO-OFDM’s high peak-to-average power ratio imposes high vulnerability to fiber-induced non-linearities. DSP-based machine learning has been considered as a promising approach for fiber non-linearity compensation without sacrificing computational complexity. In this paper, we review the existing machine learning approaches for CO-OFDM in a common framework and review the progress in this area with a focus on practical aspects and comparison with benchmark DSP solutions.https://www.mdpi.com/1999-5903/11/1/2fiber optics communicationsmachine learningartificial neural networksupport vector machineclusteringnonlinear equalizationcoherent optical OFDM |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Elias Giacoumidis Yi Lin Jinlong Wei Ivan Aldaya Athanasios Tsokanos Liam P. Barry |
| spellingShingle |
Elias Giacoumidis Yi Lin Jinlong Wei Ivan Aldaya Athanasios Tsokanos Liam P. Barry Harnessing machine learning for fiber-induced nonlinearity mitigation in long-haul coherent optical OFDM Future Internet fiber optics communications machine learning artificial neural network support vector machine clustering nonlinear equalization coherent optical OFDM |
| author_facet |
Elias Giacoumidis Yi Lin Jinlong Wei Ivan Aldaya Athanasios Tsokanos Liam P. Barry |
| author_sort |
Elias Giacoumidis |
| title |
Harnessing machine learning for fiber-induced nonlinearity mitigation in long-haul coherent optical OFDM |
| title_short |
Harnessing machine learning for fiber-induced nonlinearity mitigation in long-haul coherent optical OFDM |
| title_full |
Harnessing machine learning for fiber-induced nonlinearity mitigation in long-haul coherent optical OFDM |
| title_fullStr |
Harnessing machine learning for fiber-induced nonlinearity mitigation in long-haul coherent optical OFDM |
| title_full_unstemmed |
Harnessing machine learning for fiber-induced nonlinearity mitigation in long-haul coherent optical OFDM |
| title_sort |
harnessing machine learning for fiber-induced nonlinearity mitigation in long-haul coherent optical ofdm |
| publisher |
MDPI AG |
| series |
Future Internet |
| issn |
1999-5903 |
| publishDate |
2018-12-01 |
| description |
Coherent optical orthogonal frequency division multiplexing (CO-OFDM) has attracted a lot of interest in optical fiber communications due to its simplified digital signal processing (DSP) units, high spectral-efficiency, flexibility, and tolerance to linear impairments. However, CO-OFDM’s high peak-to-average power ratio imposes high vulnerability to fiber-induced non-linearities. DSP-based machine learning has been considered as a promising approach for fiber non-linearity compensation without sacrificing computational complexity. In this paper, we review the existing machine learning approaches for CO-OFDM in a common framework and review the progress in this area with a focus on practical aspects and comparison with benchmark DSP solutions. |
| topic |
fiber optics communications machine learning artificial neural network support vector machine clustering nonlinear equalization coherent optical OFDM |
| url |
https://www.mdpi.com/1999-5903/11/1/2 |
| work_keys_str_mv |
AT eliasgiacoumidis harnessingmachinelearningforfiberinducednonlinearitymitigationinlonghaulcoherentopticalofdm AT yilin harnessingmachinelearningforfiberinducednonlinearitymitigationinlonghaulcoherentopticalofdm AT jinlongwei harnessingmachinelearningforfiberinducednonlinearitymitigationinlonghaulcoherentopticalofdm AT ivanaldaya harnessingmachinelearningforfiberinducednonlinearitymitigationinlonghaulcoherentopticalofdm AT athanasiostsokanos harnessingmachinelearningforfiberinducednonlinearitymitigationinlonghaulcoherentopticalofdm AT liampbarry harnessingmachinelearningforfiberinducednonlinearitymitigationinlonghaulcoherentopticalofdm |
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