Multi-Task Learning Convolutional Neural Network and Optical Spectrums Enabled Optical Performance Monitoring

Yu, Chunlei; Wang, Haoyu; Ke, Changjian; Liang, Zi; Cui, Sheng; Liu, Deming

doi:10.1109/jphot.2022.3153638

Cited by 7 publications

(3 citation statements)

References 30 publications

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“…From Fig. 3(b), it can be seen that the accumulation of nonlinear noise due to the increasing launch power and transmission distance leads to spectrum broadening and an overall upward shift of the spectrum [25]. When the shape of the spectrum changes, we focus on the powers variations to set up the AH to realize the joint estimation of OSNR and nonlinear noise power with the aim of reducing the complexity.…”

Section: A Amplitude Histograms Based On Optical Spectrumsmentioning

confidence: 99%

Joint OSNR and Nonlinear Noise Power Estimation Based on Deep Learning for Coherent Optical Communication Systems

Li,

Zhang,

Zhang

et al. 2023

IEEE Photonics J.

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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.

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Section: A Amplitude Histograms Based On Optical Spectrumsmentioning

confidence: 99%

Joint OSNR and Nonlinear Noise Power Estimation Based on Deep Learning for Coherent Optical Communication Systems

Li,

Zhang,

Zhang

et al. 2023

IEEE Photonics J.

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“…The MT-ANN is not suitable for handling the HOS because the tremendous input neurons required may result in an extremely complex network structure and thus the risk of under-fitting in the training process [16]. To deal with the HOS, we recently proposed a multi-task 1D-CNN (MT-CNN) based OSA technique able to achieve an OS recognition accuracy of 100% and an OSNR estimation mean absolute error of 0.262dB [19].…”

Section: > Replace This Line With Your Manuscript Id Number (Double-c...mentioning

confidence: 99%

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

Wang

Cui

et al. 2022

IEEE Photonics J.

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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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“…However, these enhanced features will lead to the increased complexity of manipulating available resources for heterogeneous vendors and technical domains [4]. Consequently, it is necessary to implement a comprehensive and intelligent optical performance monitoring (OPM) system to ensure effective network management and diagnose faults [3][4][5][6]. There are generally two types of OPM that gained considerable attention in research: OPM at intermediate network nodes and OPM at end nodes [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Linear Regression vs. Deep Learning for Signal Quality Monitoring in Coherent Optical Systems

et al. 2022

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Error vector magnitude (EVM) is a metric for assessing the quality of m-ary quadrature amplitude modulation (mQAM) signals. Recently proposed deep learning techniques, e.g., feedforward neural networks (FFNNs) -based EVM estimation scheme leverage fast signal quality monitoring in coherent optical communication systems. Such a scheme estimates EVM from amplitude histograms (AHs) of short signal sequences captured before carrier phase recovery (CPR). In this work, we explore further complexity reduction by proposing a simple linear regression (LR) -based EVM monitoring method. We systematically compare the performance of the proposed method with the FFNN-based scheme and demonstrate its capability to infer EVM from an AH when the modulation format information is known in advance. We perform both simulation and experiment to show that the LR-based EVM estimation method achieves a comparable accuracy as the FFNN-based scheme. The technique can be embedded with modulation format identification modules to provide comprehensive signal information. Therefore, this

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Multi-Task Learning Convolutional Neural Network and Optical Spectrums Enabled Optical Performance Monitoring

Cited by 7 publications

References 30 publications

Joint OSNR and Nonlinear Noise Power Estimation Based on Deep Learning for Coherent Optical Communication Systems

Joint OSNR and Nonlinear Noise Power Estimation Based on Deep Learning for Coherent Optical Communication Systems

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

Linear Regression vs. Deep Learning for Signal Quality Monitoring in Coherent Optical Systems

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