Optical link monitoring in fibre-to-the-x passive optical network (FTTx PON): A comprehensive survey

Usman, Auwalu; Zulkifli, Nadiatulhuda; Salim, Mohd Rashidi; Khairi, K.; Azmi, Asrul Izam

doi:10.1016/j.osn.2020.100596

Cited by 24 publications

(9 citation statements)

References 141 publications

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“…This result demonstrates the high precision of the SVM model in predicting the power to be obtained by the OSA on the basis of the experimental and Optisystem simulation configurations. Some required monitoring features, as mentioned in literature, 5,40 must be observed for efficient PON network monitoring. As indicated in Table 9, these features are utilized to compare the performance of the proposed technique to existing state-of-the-art PON monitoring systems utilizing FBG sensors.…”

Section: Discussionmentioning

confidence: 99%

“…Some required monitoring features, as mentioned in literature, 5,40 must be observed for efficient PON network monitoring. As indicated in Table 9, these features are utilized to compare the performance of the proposed technique to existing state‐of‐the‐art PON monitoring systems utilizing FBG sensors.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, the OTDR fault localization process in the optical distribution network (ODN) were normally performed manually by adding the OTDR pulses from the consumer side of the network. 5 Lately, machine learning (ML) has demonstrated an enticing potential of promoting enhanced resource performance, QoS assurance, and interoperability in optical networks. 6,7 Particularly, ML allows network operators to implement knowledge-based stand-alone service delivery, 8 by modeling complex network behaviors (e.g., end-to-end transmission quality 9 and traffic profile 10 ) and learning accurate online provisioning strategies for diverse network operations that are intractable to conventional analytical techniques.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Usman

Zulkifli

Salim

et al. 2022

Int J Communication

Self Cite

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“…Consequently, traditional inefficient troubleshooting methods increase the operational cost and customer dissatisfaction. To ensure the quality and reliability of PON access network services, especially the fiber optics-based infrastructure for high-speed data and information distribution, an effective optical layer monitoring system is essential [10].…”

Section: Introductionmentioning

confidence: 99%

A PON Monitoring System Integrating Fault Detection and Localization

2022

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We propose and experimentally demonstrate an integrated passive optical network monitoring system for fault detection and localization. Fault detection technology is based on the recognition of reflection spectrum generated by each single fiber Bragg grating, which can support large-capacity user fiber fault monitoring. Fault localization method uses two different total received powers measured by an optical power meter to solve the established equation, and it shows a good accuracy compared with the OTDR measurement results. Fault detection and localization are carried out separately but share the same transmitter module. The introduction of the additional module ensures that the troubleshooting does not affect other normal user data communication. We also investigate the dynamic range requirements of the fault detection technology and the measurement error of the fault localization method. The calculation results show the required dynamic range for the recognition device can be maintained within a small range and the use of high-accuracy optical power meter can reduce the measurement error. The proposed monitoring system can simultaneously provide a large-capacity fault detection technology and high-accuracy fault localization method in a cost-sensitive passive optical network market.

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“…A passive optical network (PON) is a ber to the home (FTTH) point to a multipoint network that provides high data rate service to end-users [1], [2]. The optical distribution network (ODN) in the PON consists of passive components like optical power splitters, optical ber, couplers, etc.…”

Section: Introductionmentioning

confidence: 99%

Entirely Passive Remote Node Design And System Architecture For Bus Topology Based 80Gbps Symmetrical NG-PON2

Kaur

Tiwana

Singh

et al. 2021

Preprint

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The next generation passive optical network stage 2 (NG-PON2) is a broadcast network in which a large number of optical network units (ONUs) can be served using various network topologies. In this paper, a purely passive remote node (RN) design based on fiber bragg grating (FBG) is presented for the bus topology of a high split NG-PON2 system. The performance of the proposed node has been analysed for a symmetric 80 Gbps NG-PON2 system serving 1024 ONUs in terms of bit error rate (BER) and optical signal to noise ratio (OSNR). The working of the proposed node has been explained with the optical frequency spectrums for downstream and upstream signals. The observed OSNR levels for the ONUs operating at the 1st RN is 84.8743dB which decreases to 75.7973dB for the ONUs connected to the 8th RN. The BER level < 10-9 for both upstream and downstream confirms the correct working of the proposed node in the NG-PON2 system.

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Optical link monitoring in fibre-to-the-x passive optical network (FTTx PON): A comprehensive survey

Cited by 24 publications

References 141 publications

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

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

A PON Monitoring System Integrating Fault Detection and Localization

Entirely Passive Remote Node Design And System Architecture For Bus Topology Based 80Gbps Symmetrical NG-PON2

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