Optical Performance Monitoring in Mode Division Multiplexed Optical Networks

Saif, Waddah S.; Ragheb, Amr; Alshawi, Tariq; Alshebeili, Saleh A.

doi:10.1109/jlt.2020.3027725

Cited by 29 publications

(10 citation statements)

References 57 publications

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“…Figure 9 shows resulting IQH's AH's and constellation diagrams for different impairments. Saif et al (2021) derived 1D features from projections of IQH's on diagonal and horizontal axes.…”

Section: In-phase Quardrature Histogramsmentioning

confidence: 99%

“…OPM for few mode fibers was considered in Saif et al (2021). In this work, OSNR, CD and mode coupling were monitored with the aid of three ML algorithms i.e., SVM, random forest and CNN.…”

Section: Machine Learning Applied To Coherent Detection Systemsmentioning

confidence: 99%

“…In some cases, widely known features from these plots such as statistical means, variances, standard deviations etc, counts of occurrences per bin, eye diagram parameters like eye closure, crossing amplitude etc. have been used, while in others new features have been defined to exploit visible differences in the plots (Jargon et al, 2009a;Saif et al, 2021). Manual definition of features is a difficult task which requires experience and also makes it impossible to distinguish patterns when there are only slight differences for example, the performance of ANN's have been shown to deteriorate beyond certain OSNR's because there is very little distinction between the eye diagrams especially for higher modulation formats (Thrane et al, 2017).…”

Section: Application Of Photonic Reservoir Computing In Optical Performance Monitoringmentioning

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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“…Figure 9 shows resulting IQH's AH's and constellation diagrams for different impairments. Saif et al (2021) derived 1D features from projections of IQH's on diagonal and horizontal axes.…”

Section: In-phase Quardrature Histogramsmentioning

confidence: 99%

“…OPM for few mode fibers was considered in Saif et al (2021). In this work, OSNR, CD and mode coupling were monitored with the aid of three ML algorithms i.e., SVM, random forest and CNN.…”

Section: Machine Learning Applied To Coherent Detection Systemsmentioning

confidence: 99%

Section: Application Of Photonic Reservoir Computing In Optical Performance Monitoringmentioning

confidence: 99%

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Machine Learning-Aided Optical Performance Monitoring Techniques: A Review

2022

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“…Unlike the constellation-based scheme that relies on images [21], [22], the 2D-IQH forms densities by combining both the in-phase (I) and quadrature (Q) information on a 2D histogram (i.e. the number of joint occurrences of I and Q values) [17], [31], [40]. The 2D-IQH is the output of step 2, as described in Fig.…”

Section: Mfi Based On 2d-iqh Features and Cnnmentioning

confidence: 99%

“…On the other side, machine learning (ML) algorithms have been utilized in several fields and shown to present superior results compared to conventional digital signal processing (DSP) approaches. In fiber optical networks, there have been extensive works on the use of ML, as in routing optimization [15], fiber nonlinearities mitigation [16], optical performance monitoring (OPM) [17], [18], and MFI [8]. In MFI, the authors in [19], [20] proposed an identification technique based on artificial neural network (ANN) and amplitude histogram (AH) to identify different optical modulation formats.…”

Section: Introductionmentioning

confidence: 99%

Performance Investigation of Modulation Format Identification in Super-Channel Optical Networks

Saif

Ragheb

Nebendahl³

et al. 2022

IEEE Photonics J.

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The problem of automatic modulation format identification (MFI) is one of the main challenges in adaptive optical systems. In this work, we investigate MFI in superchannel optical networks. The investigation is conducted by considering the classification of seven multiplexed channels of 20 Gbaud, each with six commonly used modulation formats, including polarization division multiplexing (PDM)-BPSK, PDM-QPSK, and PDM-MQAM with (M=8,16,32,64). The classification performance is assessed under different values of optical signalto-noise ratio (OSNR) and in the presence of channel interference, channel chromatic dispersion, phase noise, and 1 st polarization mode dispersion (PMD). Furthermore, the effect of fiber nonlinearity on the MFI accuracy is investigated. A well-established machine learning algorithm based on histogram features and a convolutional neural network has been used in this investigation. Results indicate that accurate identification accuracy can be achieved within the OSNR range of practical systems and that the MFI accuracy of side subcarriers outperforms that of middle subcarriers at a fixed value of OSNR. The results also show that the MFI accuracy of PDM-16QAM and PDM-64QAM are affected more by channel interference than the other modulation formats, especially when the ratio of the subcarrier bandwidth to subcarriers spacing is ≥ 1.4. Finally, laboratory experiments have been conducted for validation purposes. The experimental results were found in good agreement with those achieved by simulation.

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