Joint OSNR and Nonlinear Noise Power Estimation Based on Deep Learning for Coherent Optical Communication Systems
In this paper, a joint OSNR and nonlinear noise power estimation scheme based on multi-task deep neural network (MT-DNN) is proposed with the advantages of dispersion-insensitive, modulation-format-transparent for high-speed, long-haul, multi-channel fiber-optic communication systems. Amplitude hist...
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IEEE
2023-01-01
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| Series: | IEEE Photonics Journal |
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| Online Access: | https://ieeexplore.ieee.org/document/10313964/ |
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| author | Mengyan Li Lifu Zhang Tao Zhang Guozhou Jiang Liu Yang Fengguang Luo Yongming Hu |
| author_facet | Mengyan Li Lifu Zhang Tao Zhang Guozhou Jiang Liu Yang Fengguang Luo Yongming Hu |
| author_sort | Mengyan Li |
| collection | DOAJ |
| description | In this paper, a joint OSNR and nonlinear noise power estimation scheme based on multi-task deep neural network (MT-DNN) is proposed with the advantages of dispersion-insensitive, modulation-format-transparent for high-speed, long-haul, multi-channel fiber-optic communication systems. Amplitude histograms (AHs) are generated by processing the spectrums collected with different OSNR, launch power and transmission distance by an offline spectrum preprocessing flow. The MT-DNN can automatically learn the features of the AHs to achieve OSNR and nonlinear noise power estimation, simultaneously. For 4-quadrature amplitude modulation (4QAM), 16QAM and 64QAM signals under different transmission conditions, the average MAE and RMSE are calculated for the OSNR estimate and the nonlinear noise power estimate, which are both less than 1 dB. Moreover, the resistance of OSNR estimation to amplified spontaneous emission (ASE) noise and nonlinearity, and the tolerance of nonlinear noise estimation to launch power and transmission distance are investigated, respectively. The results demonstrate that the joint OSNR and nonlinear noise power estimation scheme is insensitive to dispersion, transparent to modulation format, and has high accuracy and high tolerance. This research provides a research reference value for future optical performance monitoring of coherent optical communication systems. |
| format | Article |
| id | doaj-art-aa0abfc4ab9b4647a8a6c77b9c580be6 |
| institution | OA Journals |
| issn | 1943-0655 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | IEEE |
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| series | IEEE Photonics Journal |
| spelling | doaj-art-aa0abfc4ab9b4647a8a6c77b9c580be62025-08-20T02:38:03ZengIEEEIEEE Photonics Journal1943-06552023-01-011561810.1109/JPHOT.2023.333130210313964Joint OSNR and Nonlinear Noise Power Estimation Based on Deep Learning for Coherent Optical Communication SystemsMengyan Li0https://orcid.org/0000-0002-5475-1032Lifu Zhang1https://orcid.org/0009-0007-1381-4790Tao Zhang2https://orcid.org/0009-0001-2685-0113Guozhou Jiang3https://orcid.org/0009-0006-2808-7883Liu Yang4https://orcid.org/0000-0001-6588-9902Fengguang Luo5https://orcid.org/0000-0001-6713-7579Yongming Hu6https://orcid.org/0000-0003-4715-0172School of Microelectronics, Hubei Key Laboratory of Micro-Nanoelectronic Materials and Devices, Hubei University, Wuhan, ChinaSchool of Microelectronics, Hubei Key Laboratory of Micro-Nanoelectronic Materials and Devices, Hubei University, Wuhan, ChinaSchool of Microelectronics, Hubei Key Laboratory of Micro-Nanoelectronic Materials and Devices, Hubei University, Wuhan, ChinaSchool of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, ChinaSchool of Microelectronics, Hubei Key Laboratory of Micro-Nanoelectronic Materials and Devices, Hubei University, Wuhan, ChinaSchool of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, ChinaSchool of Microelectronics, Hubei Key Laboratory of Micro-Nanoelectronic Materials and Devices, Hubei University, Wuhan, ChinaIn this paper, a joint OSNR and nonlinear noise power estimation scheme based on multi-task deep neural network (MT-DNN) is proposed with the advantages of dispersion-insensitive, modulation-format-transparent for high-speed, long-haul, multi-channel fiber-optic communication systems. Amplitude histograms (AHs) are generated by processing the spectrums collected with different OSNR, launch power and transmission distance by an offline spectrum preprocessing flow. The MT-DNN can automatically learn the features of the AHs to achieve OSNR and nonlinear noise power estimation, simultaneously. For 4-quadrature amplitude modulation (4QAM), 16QAM and 64QAM signals under different transmission conditions, the average MAE and RMSE are calculated for the OSNR estimate and the nonlinear noise power estimate, which are both less than 1 dB. Moreover, the resistance of OSNR estimation to amplified spontaneous emission (ASE) noise and nonlinearity, and the tolerance of nonlinear noise estimation to launch power and transmission distance are investigated, respectively. The results demonstrate that the joint OSNR and nonlinear noise power estimation scheme is insensitive to dispersion, transparent to modulation format, and has high accuracy and high tolerance. This research provides a research reference value for future optical performance monitoring of coherent optical communication systems.https://ieeexplore.ieee.org/document/10313964/Coherent optical communicationsoptical performance monitoringoptical signal-to-noise rationonlinear noise powermulti-task deep neural networks |
| spellingShingle | Mengyan Li Lifu Zhang Tao Zhang Guozhou Jiang Liu Yang Fengguang Luo Yongming Hu Joint OSNR and Nonlinear Noise Power Estimation Based on Deep Learning for Coherent Optical Communication Systems IEEE Photonics Journal Coherent optical communications optical performance monitoring optical signal-to-noise ratio nonlinear noise power multi-task deep neural networks |
| title | Joint OSNR and Nonlinear Noise Power Estimation Based on Deep Learning for Coherent Optical Communication Systems |
| title_full | Joint OSNR and Nonlinear Noise Power Estimation Based on Deep Learning for Coherent Optical Communication Systems |
| title_fullStr | Joint OSNR and Nonlinear Noise Power Estimation Based on Deep Learning for Coherent Optical Communication Systems |
| title_full_unstemmed | Joint OSNR and Nonlinear Noise Power Estimation Based on Deep Learning for Coherent Optical Communication Systems |
| title_short | Joint OSNR and Nonlinear Noise Power Estimation Based on Deep Learning for Coherent Optical Communication Systems |
| title_sort | joint osnr and nonlinear noise power estimation based on deep learning for coherent optical communication systems |
| topic | Coherent optical communications optical performance monitoring optical signal-to-noise ratio nonlinear noise power multi-task deep neural networks |
| url | https://ieeexplore.ieee.org/document/10313964/ |
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