Multi-Functional Optical Spectrum Analysis Using Multi-Task Cascaded Neural Networks

In the optical communication systems, the optical spectrum (OS) provides useful informations for optical performance monitoring and optical link diagnosis. In this paper, we investigate the neural-network based OS analysis (OSA) techniques able to simultaneously recognize the OS and estimate the opt...
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Autor*in:	Haoyu Wang [verfasserIn] Sheng Cui [verfasserIn] Changjian Ke [verfasserIn] Chenglong Yu [verfasserIn] Zi Liang [verfasserIn] Deming Liu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Optical spectrum optical performance monitoring convolutional neural networks artificial neural networks multi-task learning spectrum analysis

Übergeordnetes Werk:	In: IEEE Photonics Journal - IEEE, 2015, 14(2022), 4, Seite 9
Übergeordnetes Werk:	volume:14 ; year:2022 ; number:4 ; pages:9

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/JPHOT.2022.3187648

Katalog-ID:	DOAJ032975953

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520			\|a In the optical communication systems, the optical spectrum (OS) provides useful informations for optical performance monitoring and optical link diagnosis. In this paper, we investigate the neural-network based OS analysis (OSA) techniques able to simultaneously recognize the OS and estimate the optical signal to noise ratio (OSNR), cascaded filtering distortion (CFD) and carrier wavelength drift (CWD). We demonstrate that, compared with the multi-task artificial neural network (MT-ANN) and convolutional neural network (MT-CNN), the proposed multi-task cascaded ANNs (CANN) and cascaded CNNs (CCNN) can greatly improve the OSA performance and accelerate the training process by exploiting specific features and loss functions for different tasks. The CCNN able to deal with the high-resolution OS (HOS) can further improve the OSA performance compared with the CANN. The averaged OS recognition accuracy and OSNR, CFD and CWD estimation errors obtained with the CCNN (CANN) for the 15 kinds of optical signals are 99.32% (97.07%), 0.36 (0.55) dB, 0.24 (0.32) and 0.04 (0.06) GHz, respectively, even when the various OS distortions are present. The proposed CANN and CCNN with a better versatility, higher OSA accuracy and faster convergence speed are promising enabling techniques for the future intelligent OSA systems.
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allfields	10.1109/JPHOT.2022.3187648 doi (DE-627)DOAJ032975953 (DE-599)DOAJ9276eb024082438cad65abaf956db0f9 DE-627 ger DE-627 rakwb eng TA1501-1820 QC350-467 Haoyu Wang verfasserin aut Multi-Functional Optical Spectrum Analysis Using Multi-Task Cascaded Neural Networks 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In the optical communication systems, the optical spectrum (OS) provides useful informations for optical performance monitoring and optical link diagnosis. In this paper, we investigate the neural-network based OS analysis (OSA) techniques able to simultaneously recognize the OS and estimate the optical signal to noise ratio (OSNR), cascaded filtering distortion (CFD) and carrier wavelength drift (CWD). We demonstrate that, compared with the multi-task artificial neural network (MT-ANN) and convolutional neural network (MT-CNN), the proposed multi-task cascaded ANNs (CANN) and cascaded CNNs (CCNN) can greatly improve the OSA performance and accelerate the training process by exploiting specific features and loss functions for different tasks. The CCNN able to deal with the high-resolution OS (HOS) can further improve the OSA performance compared with the CANN. The averaged OS recognition accuracy and OSNR, CFD and CWD estimation errors obtained with the CCNN (CANN) for the 15 kinds of optical signals are 99.32% (97.07%), 0.36 (0.55) dB, 0.24 (0.32) and 0.04 (0.06) GHz, respectively, even when the various OS distortions are present. The proposed CANN and CCNN with a better versatility, higher OSA accuracy and faster convergence speed are promising enabling techniques for the future intelligent OSA systems. Optical spectrum optical performance monitoring convolutional neural networks artificial neural networks multi-task learning spectrum analysis Applied optics. Photonics Optics. Light Sheng Cui verfasserin aut Changjian Ke verfasserin aut Chenglong Yu verfasserin aut Zi Liang verfasserin aut Deming Liu verfasserin aut In IEEE Photonics Journal IEEE, 2015 14(2022), 4, Seite 9 (DE-627)600310272 (DE-600)2495610-7 19430655 nnns volume:14 year:2022 number:4 pages:9 https://doi.org/10.1109/JPHOT.2022.3187648 kostenfrei https://doaj.org/article/9276eb024082438cad65abaf956db0f9 kostenfrei https://ieeexplore.ieee.org/document/9811258/ kostenfrei https://doaj.org/toc/1943-0655 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2022 4 9
spelling	10.1109/JPHOT.2022.3187648 doi (DE-627)DOAJ032975953 (DE-599)DOAJ9276eb024082438cad65abaf956db0f9 DE-627 ger DE-627 rakwb eng TA1501-1820 QC350-467 Haoyu Wang verfasserin aut Multi-Functional Optical Spectrum Analysis Using Multi-Task Cascaded Neural Networks 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In the optical communication systems, the optical spectrum (OS) provides useful informations for optical performance monitoring and optical link diagnosis. In this paper, we investigate the neural-network based OS analysis (OSA) techniques able to simultaneously recognize the OS and estimate the optical signal to noise ratio (OSNR), cascaded filtering distortion (CFD) and carrier wavelength drift (CWD). We demonstrate that, compared with the multi-task artificial neural network (MT-ANN) and convolutional neural network (MT-CNN), the proposed multi-task cascaded ANNs (CANN) and cascaded CNNs (CCNN) can greatly improve the OSA performance and accelerate the training process by exploiting specific features and loss functions for different tasks. The CCNN able to deal with the high-resolution OS (HOS) can further improve the OSA performance compared with the CANN. The averaged OS recognition accuracy and OSNR, CFD and CWD estimation errors obtained with the CCNN (CANN) for the 15 kinds of optical signals are 99.32% (97.07%), 0.36 (0.55) dB, 0.24 (0.32) and 0.04 (0.06) GHz, respectively, even when the various OS distortions are present. The proposed CANN and CCNN with a better versatility, higher OSA accuracy and faster convergence speed are promising enabling techniques for the future intelligent OSA systems. Optical spectrum optical performance monitoring convolutional neural networks artificial neural networks multi-task learning spectrum analysis Applied optics. Photonics Optics. Light Sheng Cui verfasserin aut Changjian Ke verfasserin aut Chenglong Yu verfasserin aut Zi Liang verfasserin aut Deming Liu verfasserin aut In IEEE Photonics Journal IEEE, 2015 14(2022), 4, Seite 9 (DE-627)600310272 (DE-600)2495610-7 19430655 nnns volume:14 year:2022 number:4 pages:9 https://doi.org/10.1109/JPHOT.2022.3187648 kostenfrei https://doaj.org/article/9276eb024082438cad65abaf956db0f9 kostenfrei https://ieeexplore.ieee.org/document/9811258/ kostenfrei https://doaj.org/toc/1943-0655 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2022 4 9
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allfieldsSound	10.1109/JPHOT.2022.3187648 doi (DE-627)DOAJ032975953 (DE-599)DOAJ9276eb024082438cad65abaf956db0f9 DE-627 ger DE-627 rakwb eng TA1501-1820 QC350-467 Haoyu Wang verfasserin aut Multi-Functional Optical Spectrum Analysis Using Multi-Task Cascaded Neural Networks 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In the optical communication systems, the optical spectrum (OS) provides useful informations for optical performance monitoring and optical link diagnosis. In this paper, we investigate the neural-network based OS analysis (OSA) techniques able to simultaneously recognize the OS and estimate the optical signal to noise ratio (OSNR), cascaded filtering distortion (CFD) and carrier wavelength drift (CWD). We demonstrate that, compared with the multi-task artificial neural network (MT-ANN) and convolutional neural network (MT-CNN), the proposed multi-task cascaded ANNs (CANN) and cascaded CNNs (CCNN) can greatly improve the OSA performance and accelerate the training process by exploiting specific features and loss functions for different tasks. The CCNN able to deal with the high-resolution OS (HOS) can further improve the OSA performance compared with the CANN. The averaged OS recognition accuracy and OSNR, CFD and CWD estimation errors obtained with the CCNN (CANN) for the 15 kinds of optical signals are 99.32% (97.07%), 0.36 (0.55) dB, 0.24 (0.32) and 0.04 (0.06) GHz, respectively, even when the various OS distortions are present. The proposed CANN and CCNN with a better versatility, higher OSA accuracy and faster convergence speed are promising enabling techniques for the future intelligent OSA systems. Optical spectrum optical performance monitoring convolutional neural networks artificial neural networks multi-task learning spectrum analysis Applied optics. Photonics Optics. Light Sheng Cui verfasserin aut Changjian Ke verfasserin aut Chenglong Yu verfasserin aut Zi Liang verfasserin aut Deming Liu verfasserin aut In IEEE Photonics Journal IEEE, 2015 14(2022), 4, Seite 9 (DE-627)600310272 (DE-600)2495610-7 19430655 nnns volume:14 year:2022 number:4 pages:9 https://doi.org/10.1109/JPHOT.2022.3187648 kostenfrei https://doaj.org/article/9276eb024082438cad65abaf956db0f9 kostenfrei https://ieeexplore.ieee.org/document/9811258/ kostenfrei https://doaj.org/toc/1943-0655 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2022 4 9
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callnumber-first	T - Technology
author	Haoyu Wang
spellingShingle	Haoyu Wang misc TA1501-1820 misc QC350-467 misc Optical spectrum misc optical performance monitoring misc convolutional neural networks misc artificial neural networks misc multi-task learning misc spectrum analysis misc Applied optics. Photonics misc Optics. Light Multi-Functional Optical Spectrum Analysis Using Multi-Task Cascaded Neural Networks
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topic_title	TA1501-1820 QC350-467 Multi-Functional Optical Spectrum Analysis Using Multi-Task Cascaded Neural Networks Optical spectrum optical performance monitoring convolutional neural networks artificial neural networks multi-task learning spectrum analysis
topic	misc TA1501-1820 misc QC350-467 misc Optical spectrum misc optical performance monitoring misc convolutional neural networks misc artificial neural networks misc multi-task learning misc spectrum analysis misc Applied optics. Photonics misc Optics. Light
topic_unstemmed	misc TA1501-1820 misc QC350-467 misc Optical spectrum misc optical performance monitoring misc convolutional neural networks misc artificial neural networks misc multi-task learning misc spectrum analysis misc Applied optics. Photonics misc Optics. Light
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title	Multi-Functional Optical Spectrum Analysis Using Multi-Task Cascaded Neural Networks
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title_full	Multi-Functional Optical Spectrum Analysis Using Multi-Task Cascaded Neural Networks
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title_sort	multi-functional optical spectrum analysis using multi-task cascaded neural networks
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title_auth	Multi-Functional Optical Spectrum Analysis Using Multi-Task Cascaded Neural Networks
abstract	In the optical communication systems, the optical spectrum (OS) provides useful informations for optical performance monitoring and optical link diagnosis. In this paper, we investigate the neural-network based OS analysis (OSA) techniques able to simultaneously recognize the OS and estimate the optical signal to noise ratio (OSNR), cascaded filtering distortion (CFD) and carrier wavelength drift (CWD). We demonstrate that, compared with the multi-task artificial neural network (MT-ANN) and convolutional neural network (MT-CNN), the proposed multi-task cascaded ANNs (CANN) and cascaded CNNs (CCNN) can greatly improve the OSA performance and accelerate the training process by exploiting specific features and loss functions for different tasks. The CCNN able to deal with the high-resolution OS (HOS) can further improve the OSA performance compared with the CANN. The averaged OS recognition accuracy and OSNR, CFD and CWD estimation errors obtained with the CCNN (CANN) for the 15 kinds of optical signals are 99.32% (97.07%), 0.36 (0.55) dB, 0.24 (0.32) and 0.04 (0.06) GHz, respectively, even when the various OS distortions are present. The proposed CANN and CCNN with a better versatility, higher OSA accuracy and faster convergence speed are promising enabling techniques for the future intelligent OSA systems.
abstractGer	In the optical communication systems, the optical spectrum (OS) provides useful informations for optical performance monitoring and optical link diagnosis. In this paper, we investigate the neural-network based OS analysis (OSA) techniques able to simultaneously recognize the OS and estimate the optical signal to noise ratio (OSNR), cascaded filtering distortion (CFD) and carrier wavelength drift (CWD). We demonstrate that, compared with the multi-task artificial neural network (MT-ANN) and convolutional neural network (MT-CNN), the proposed multi-task cascaded ANNs (CANN) and cascaded CNNs (CCNN) can greatly improve the OSA performance and accelerate the training process by exploiting specific features and loss functions for different tasks. The CCNN able to deal with the high-resolution OS (HOS) can further improve the OSA performance compared with the CANN. The averaged OS recognition accuracy and OSNR, CFD and CWD estimation errors obtained with the CCNN (CANN) for the 15 kinds of optical signals are 99.32% (97.07%), 0.36 (0.55) dB, 0.24 (0.32) and 0.04 (0.06) GHz, respectively, even when the various OS distortions are present. The proposed CANN and CCNN with a better versatility, higher OSA accuracy and faster convergence speed are promising enabling techniques for the future intelligent OSA systems.
abstract_unstemmed	In the optical communication systems, the optical spectrum (OS) provides useful informations for optical performance monitoring and optical link diagnosis. In this paper, we investigate the neural-network based OS analysis (OSA) techniques able to simultaneously recognize the OS and estimate the optical signal to noise ratio (OSNR), cascaded filtering distortion (CFD) and carrier wavelength drift (CWD). We demonstrate that, compared with the multi-task artificial neural network (MT-ANN) and convolutional neural network (MT-CNN), the proposed multi-task cascaded ANNs (CANN) and cascaded CNNs (CCNN) can greatly improve the OSA performance and accelerate the training process by exploiting specific features and loss functions for different tasks. The CCNN able to deal with the high-resolution OS (HOS) can further improve the OSA performance compared with the CANN. The averaged OS recognition accuracy and OSNR, CFD and CWD estimation errors obtained with the CCNN (CANN) for the 15 kinds of optical signals are 99.32% (97.07%), 0.36 (0.55) dB, 0.24 (0.32) and 0.04 (0.06) GHz, respectively, even when the various OS distortions are present. The proposed CANN and CCNN with a better versatility, higher OSA accuracy and faster convergence speed are promising enabling techniques for the future intelligent OSA systems.
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title_short	Multi-Functional Optical Spectrum Analysis Using Multi-Task Cascaded Neural Networks
url	https://doi.org/10.1109/JPHOT.2022.3187648 https://doaj.org/article/9276eb024082438cad65abaf956db0f9 https://ieeexplore.ieee.org/document/9811258/ https://doaj.org/toc/1943-0655
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author2	Sheng Cui Changjian Ke Chenglong Yu Zi Liang Deming Liu
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up_date	2024-07-03T15:08:28.026Z
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