Joint and Accurate OSNR Estimation and Modulation Format Identification Scheme Using the Feature-Based ANN

Xiang, Qian; Yang, Yanfu; Yao, Yong

doi:10.1109/jphot.2019.2929913

Cited by 21 publications

(11 citation statements)

References 26 publications

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“…A 3-layer ANN was also shown in Xiang et al (2019), and Xiang et al ( 2021) that achieved 100% MFI accuracy for different values of OSNR between 10-28 dB for 5 modulation formats.…”

Section: Recognition Of Modulation Formatmentioning

confidence: 93%

“…A single ANN was applied in Xiang et al (2019) to jointly monitor the MF and OSNR for a 28 GS/s PDM QPSK and 8, 16 and 64 QAM signals over the OSNR range of 10-16, 12-18,15-22 and 22-29 dB, respectively. Their ANN had 50 hidden neurons and took as input two statistical features derived from the amplitude of the signals i.e., kurtosis and variance.…”

Section: Machine Learning Applied To Coherent Detection Systemsmentioning

confidence: 99%

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Machine Learning-Aided Optical Performance Monitoring Techniques: A Review

2022

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Future communication systems are faced with increased demand for high capacity, dynamic bandwidth, reliability and heterogeneous traffic. To meet these requirements, networks have become more complex and thus require new design methods and monitoring techniques, as they evolve towards becoming autonomous. Machine learning has come to the forefront in recent years as a promising technology to aid in this evolution. Optical fiber communications can already provide the high capacity required for most applications, however, there is a need for increased scalability and adaptability to changing user demands and link conditions. Accurate performance monitoring is an integral part of this transformation. In this paper, we review optical performance monitoring techniques where machine learning algorithms have been applied. Moreover, since many performance monitoring approaches in the optical domain depend on knowledge of the signal type, we also review work for modulation format recognition and bitrate identification. We additionally briefly introduce a neuromorphic approach as an emerging technique that has only recently been applied to this domain.

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“…A 3-layer ANN was also shown in Xiang et al (2019), and Xiang et al ( 2021) that achieved 100% MFI accuracy for different values of OSNR between 10-28 dB for 5 modulation formats.…”

Section: Recognition Of Modulation Formatmentioning

confidence: 93%

Section: Machine Learning Applied To Coherent Detection Systemsmentioning

confidence: 99%

Machine Learning-Aided Optical Performance Monitoring Techniques: A Review

2022

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“…ML-based algorithms were able to provide better results when dealing with non-linear behavior. Also, most of the DSP-based techniques are based on the training sequences that sacrifice SE [29], which is not the case with ML-based approaches. Additionally, recent developments in ML technology have provided new techniques (e.g.…”

Section: Introductionmentioning

confidence: 99%

Optical Performance Monitoring in Mode Division Multiplexed Optical Networks

Saif

Ragheb

Alshawi

et al. 2021

J. Lightwave Technol.

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This paper considers, for the first time, optical performance monitoring (OPM) in few mode fiber (FMF)-based optical networks. One dimensional (1D) features vector, extracted by projecting a two-dimensional (2D) asynchronous in-phase quadrature histogram (IQH), and the 2D IQH are proposed to achieve OPM in FMF-based network. Three machine learning algorithms are employed for OPM and their performances are compared. These include support vector machine, random forest algorithm, and convolutional neural network. Extensive simulations are conducted to monitor optical to signal ratio (OSNR), chromatic dispersion (CD), and mode coupling (MC) for dual polarization-quadrature phase shift keying (DP-QPSK) at 10, 12, 16, 20, and 28 Gbaud transmission speeds. Besides, M-ary quadrature amplitude modulation (M = 8 and 16) is considered. Also, the OPM accuracy is investigated under different FMF channel conditions including phase noise and polarization mode dispersion. Simulation results show that the proposed 1D projection features vector provides better OPM results than those of the widely used asynchronous amplitude histogram (AAH) features. Furthermore, it has been found that the 2D IQH features outperform the 1D projection features but require larger number of features samples. Additionally, the effect of fiber nonlinearity on the OPM accuracy is investigated. Finally, OPM using the 2D IQH features has been verified experimentally for 10 Gbaud DP-QPSK signal. The obtained results show a good agreement between both simulation and experimental findings.

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“…Modulation format is also a key parameter to be recognized since it is related to the optimized digital signal processing (DSP) in coherent receiver and elastic bandwidth access for optical network [5,6]. Recently, there is growing interest in deep learning (DL) which has been demonstrated its feasibility to realize different parameters' monitoring and overcome the bottleneck of monitoring when different impairments are physically inseparable in traditional OPM [7][8][9][10]. Most of the neural networks are used separately to monitor the modulation format or the OSNR, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…The MTL with the amplitude histograms (AHs) as features has been introduced to simultaneously realize modulation format identification and OSNR monitoring [8]. Besides the AHs, kurtosis, amplitude noise or cumulative distribution function of the received signal can also be used for feature extraction [9][10][11][12][13]. However, the MFI and OSNR have different properties.…”

Section: Introductionmentioning

confidence: 99%

Joint Modulation Format Identification and OSNR Monitoring Using Cascaded Neural Network With Transfer Learning

Zhang

et al. 2021

IEEE Photonics J.

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We propose a cascaded neural network (NN) to simultaneously identify the modulation formats and monitor the optical-signal-to-noise ratio (OSNR). In the second-level network, it is a single deep NN (DNN) rather than multiple sub-networks, which makes the architecture more compact and can save the resource for real implementation. However, since the data set is constituted from all modulation formats, the universality can be guaranteed but not for the accuracy and the complexity. To accelerate the estimation process and improve the accuracy, we introduce the transfer learning (TL) and reconstruct the data set with a part from all of the modulation formats for universality and another part from a specific modulation format for TL to pursue higher accuracy. In the experiment, we compare the proposed cascaded single neural network (CSNN) with or without TL, cascaded multiple neural networks (CMNN) and adaptive multi-task learning (MTL) for MFI and OSNR monitoring. In the first-level NN, all of the three schemes can achieve the accuracy of MFI as 100 %. In the second-level NN, the CSNN with TL (TL-CSNN) can significantly improve the training speed and decline the RMSE of 0.19 dB compared with CSNN without TL. The TL-CSNN also has faster convergence speed and is more stable compared with CMNN and adaptive MTL.

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Joint and Accurate OSNR Estimation and Modulation Format Identification Scheme Using the Feature-Based ANN

Cited by 21 publications

References 26 publications

Machine Learning-Aided Optical Performance Monitoring Techniques: A Review

Machine Learning-Aided Optical Performance Monitoring Techniques: A Review

Optical Performance Monitoring in Mode Division Multiplexed Optical Networks

Joint Modulation Format Identification and OSNR Monitoring Using Cascaded Neural Network With Transfer Learning

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