Low-Complexity Adaptive Chromatic Dispersion Estimation Scheme Using Machine Learning for Coherent Long-Reach Passive Optical Networks
In the coherent long-reach passive optical networks (LRPON), it is crucial to propose cost-effective digital signal processing (DSP) technologies to reduce the overall complexity and power consumption. This paper has proposed a low-complexity chromatic dispersion (CD) estimation scheme based on deep...
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IEEE
2019-01-01
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| Series: | IEEE Photonics Journal |
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| Online Access: | https://ieeexplore.ieee.org/document/8808893/ |
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| author | Jin Li Danshi Wang Min Zhang |
| author_facet | Jin Li Danshi Wang Min Zhang |
| author_sort | Jin Li |
| collection | DOAJ |
| description | In the coherent long-reach passive optical networks (LRPON), it is crucial to propose cost-effective digital signal processing (DSP) technologies to reduce the overall complexity and power consumption. This paper has proposed a low-complexity chromatic dispersion (CD) estimation scheme based on deep neural networks (DNN) and the error vector magnitude (EVM). To add comparisons, the performances of CD estimation schemes using other two well-known machine learning algorithms including the k-nearest neighbor (KNN) and the decision tree (DT) have also been investigated. The simulation results show that the proposed CD estimation scheme is effective in the coherent LRPON with the quadrature phase shift keying (QPSK) and 16-ary quadrature amplitude modulation (QAM) systems at 14Gbaud rate, 28Gbaud rate and 56Gbaud rate. The comprehensive performances of the DNN outperform those of the KNN and the DT. The mean estimation error of the DNN is less than 20ps/nm within the 100 km access distance in the 28Gbaud QPSK/16QAM systems. What's more, compared with classical methods using the CD scanning and frequent domain equalizers (FDE), the computation complexity of the proposed CD estimation scheme based on the DNN-EVM has been respectively reduced by 72.3 times, 86.7 times and 2.8 times about the amount of multipliers, adders and comparators. |
| format | Article |
| id | doaj-art-cd4c7e1fee8d4216b6d4d1403e0adfe8 |
| institution | Kabale University |
| issn | 1943-0655 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | IEEE |
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| series | IEEE Photonics Journal |
| spelling | doaj-art-cd4c7e1fee8d4216b6d4d1403e0adfe82025-08-20T03:32:54ZengIEEEIEEE Photonics Journal1943-06552019-01-0111511110.1109/JPHOT.2019.29364268808893Low-Complexity Adaptive Chromatic Dispersion Estimation Scheme Using Machine Learning for Coherent Long-Reach Passive Optical NetworksJin Li0https://orcid.org/0000-0001-9089-6418Danshi Wang1https://orcid.org/0000-0001-9815-4013Min Zhang2State Key Laboratory of Information Photonics and Optical Communications, Beijing University of Posts and Telecommunications, Beijing, ChinaState Key Laboratory of Information Photonics and Optical Communications, Beijing University of Posts and Telecommunications, Beijing, ChinaState Key Laboratory of Information Photonics and Optical Communications, Beijing University of Posts and Telecommunications, Beijing, ChinaIn the coherent long-reach passive optical networks (LRPON), it is crucial to propose cost-effective digital signal processing (DSP) technologies to reduce the overall complexity and power consumption. This paper has proposed a low-complexity chromatic dispersion (CD) estimation scheme based on deep neural networks (DNN) and the error vector magnitude (EVM). To add comparisons, the performances of CD estimation schemes using other two well-known machine learning algorithms including the k-nearest neighbor (KNN) and the decision tree (DT) have also been investigated. The simulation results show that the proposed CD estimation scheme is effective in the coherent LRPON with the quadrature phase shift keying (QPSK) and 16-ary quadrature amplitude modulation (QAM) systems at 14Gbaud rate, 28Gbaud rate and 56Gbaud rate. The comprehensive performances of the DNN outperform those of the KNN and the DT. The mean estimation error of the DNN is less than 20ps/nm within the 100 km access distance in the 28Gbaud QPSK/16QAM systems. What's more, compared with classical methods using the CD scanning and frequent domain equalizers (FDE), the computation complexity of the proposed CD estimation scheme based on the DNN-EVM has been respectively reduced by 72.3 times, 86.7 times and 2.8 times about the amount of multipliers, adders and comparators.https://ieeexplore.ieee.org/document/8808893/Chromatic dispersion estimationcoherent passive optical networksmachine learningdigital signal processinglow complexity. |
| spellingShingle | Jin Li Danshi Wang Min Zhang Low-Complexity Adaptive Chromatic Dispersion Estimation Scheme Using Machine Learning for Coherent Long-Reach Passive Optical Networks IEEE Photonics Journal Chromatic dispersion estimation coherent passive optical networks machine learning digital signal processing low complexity. |
| title | Low-Complexity Adaptive Chromatic Dispersion Estimation Scheme Using Machine Learning for Coherent Long-Reach Passive Optical Networks |
| title_full | Low-Complexity Adaptive Chromatic Dispersion Estimation Scheme Using Machine Learning for Coherent Long-Reach Passive Optical Networks |
| title_fullStr | Low-Complexity Adaptive Chromatic Dispersion Estimation Scheme Using Machine Learning for Coherent Long-Reach Passive Optical Networks |
| title_full_unstemmed | Low-Complexity Adaptive Chromatic Dispersion Estimation Scheme Using Machine Learning for Coherent Long-Reach Passive Optical Networks |
| title_short | Low-Complexity Adaptive Chromatic Dispersion Estimation Scheme Using Machine Learning for Coherent Long-Reach Passive Optical Networks |
| title_sort | low complexity adaptive chromatic dispersion estimation scheme using machine learning for coherent long reach passive optical networks |
| topic | Chromatic dispersion estimation coherent passive optical networks machine learning digital signal processing low complexity. |
| url | https://ieeexplore.ieee.org/document/8808893/ |
| work_keys_str_mv | AT jinli lowcomplexityadaptivechromaticdispersionestimationschemeusingmachinelearningforcoherentlongreachpassiveopticalnetworks AT danshiwang lowcomplexityadaptivechromaticdispersionestimationschemeusingmachinelearningforcoherentlongreachpassiveopticalnetworks AT minzhang lowcomplexityadaptivechromaticdispersionestimationschemeusingmachinelearningforcoherentlongreachpassiveopticalnetworks |