Soft Failure Localization During Commissioning Testing and Lightpath Operation

Vela, Alba P.; Shariati, Behnam; Ruiz, Marc; Cugini, F.; Castro, Alberto; Lu, Hanchao; Proietti, Roberto; Comellas, Jaume; Castoldi, P.; Yoo, S. J. B.; Velasco, Luis

doi:10.1364/jocn.10.000a27

Cited by 104 publications

(101 citation statements)

References 18 publications

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“…Another notable use case for failure detection in optical networks using ML concepts appear in [98]. Two algorithms are proposed viz., Testing optIcal Switching at connection SetUp time (TISSUE) and FailurE causE Localization for optIcal NetworkinG (FEELING).…”

Section: B Failure Managementmentioning

confidence: 99%

“…If the differences between the slopes of the estimated and theoretical BER is above a certain threshold a failure is anticipated. While it is not clear from [98] whether the estimation of BER in the TISSUE algorithm is based on ML methods, the FEELING algorithm applies two very well-known ML methods viz., decision tree and SVM.…”

Section: B Failure Managementmentioning

confidence: 99%

“…In FEELING, the first step is to process the input dataset in the form of ordered pairs of frequency and power for each optical signal and transform them into a set of features. The features include some primary features like the power levels across the central frequency of the signal and also the power around other cut-off points of the signal spectrum (interested readers are encouraged to look into [98] for further details). In context of the FEELING algorithm, some secondary features are also defined in [98] which are linear combinations of the primary features.…”

Section: B Failure Managementmentioning

confidence: 99%

“…The features include some primary features like the power levels across the central frequency of the signal and also the power around other cut-off points of the signal spectrum (interested readers are encouraged to look into [98] for further details). In context of the FEELING algorithm, some secondary features are also defined in [98] which are linear combinations of the primary features. The feature-extraction process is undertaken by a module named FeX.…”

Section: B Failure Managementmentioning

confidence: 99%

“…A similar multi-ML algorithm based framework like [98] Fig. 11: The failure detection and identification framework adopted in [99].…”

Section: B Failure Managementmentioning

confidence: 99%

See 4 more Smart Citations

An Overview on Application of Machine Learning Techniques in Optical Networks

Musumeci

Rottondi

Nag

et al. 2019

IEEE Commun. Surv. Tutorials

483

226

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Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, Machine Learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management.The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing and compensation of nonlinear effects in optical fiber propagation.In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude the paper proposing new possible research directions.

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Section: B Failure Managementmentioning

confidence: 99%

Section: B Failure Managementmentioning

confidence: 99%

Section: B Failure Managementmentioning

confidence: 99%

Section: B Failure Managementmentioning

confidence: 99%

“…A similar multi-ML algorithm based framework like [98] Fig. 11: The failure detection and identification framework adopted in [99].…”

Section: B Failure Managementmentioning

confidence: 99%

See 3 more Smart Citations

An Overview on Application of Machine Learning Techniques in Optical Networks

Musumeci

Rottondi

Nag

et al. 2019

IEEE Commun. Surv. Tutorials

483

226

View full text Add to dashboard Cite

show abstract

Fault monitoring in passive optical network through the integration of machine learning and fiber sensors

Usman

Zulkifli

Salim

et al. 2022

Int J Communication

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Summary As the deployment of fiber‐based broadband networks continues to accelerate, the number of network facilities too is increasing exponentially. The network of optical fiber cables keeps growing as the number of passive optical network (PON) customers increases, eventually leading to unforeseen faults. Several solutions are offered for monitoring the optical link in PON with the optical time‐domain reflectometer (OTDR) as the most common for point‐to‐point optical link characterization. However, the OTDR approach has been found to be inadequate for point to multipoint network fault characterizations due to numerous back‐reflected signals converging at the power splitter that cannot be identified simultaneously by the OTDR detector. Several machine learning (ML) methods have recently been introduced for successful monitoring of optical communication links, but much of the ML technique depends on data from network transceivers to train ML algorithms to identify and detect faults. However, the use of data information for monitoring purposes can have an impact on the consistency of the services offered. In this article, we consider the deployment of the fiber Bragg grating sensor to acquire the monitoring data samples used to train the ML technique for effective link characterization. The proposed solution has the advantage of having a separate monitoring source that is independent of the data transmission signal and guarantees transparent transmission of information. The proposed ML‐based technique shows up to 99% precision for the identification of fiber defect in the PON optical distribution network.

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Predicting the actual location of faults in underground optical networks using linear regression

Nyarko‐Boateng

Adekoya

Weyori

2020

Engineering Reports

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Optical cables are enormous transmission media that carry high‐speed data across transatlantic, intercontinental, international boundaries, and cities. The optical cable is essential in data communication. The cable has become an indispensable component in optical communications infrastructure; hence, conscious efforts are always adopted to prevent or minimize faults in the optical network infrastructure. Typically, tracing fault in the underground optical network has been difficult even though the optical time‐domain reflectometer (OTDR) has been used to measure the distance of faults in the underground fiber cable. The methodologies deployed in the reviewed literature indicate a vast gap between the fault distance measured by the OTDR and the actual distance of fault. This paper observed the difficulties involved in tracing the actual spot of fault in the underground optical networks. The difficulty of tracing these underground faults mostly result in an undue delay and loss of revenue. This research presents a machine learning approach to predict the actual location of a fiber cable fault in an underground optical transmission link. Linear regression in the python sci‐kit learn library was used to predict the actual location of a fault in an underground optical network. The mean square error and MAE evaluation matrix used provided good accuracy results of 0.061291 and 0.080143, respectively. The result obtained in this paper indicates that faults in underground optical networks can be found quickly to avoid the delays in the fault tracing process, which leads to an excessive revenue loss.

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Soft Failure Localization During Commissioning Testing and Lightpath Operation

Cited by 104 publications

References 18 publications

An Overview on Application of Machine Learning Techniques in Optical Networks

An Overview on Application of Machine Learning Techniques in Optical Networks

Fault monitoring in passive optical network through the integration of machine learning and fiber sensors

Predicting the actual location of faults in underground optical networks using linear regression

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