Field trial of Machine-Learning-assisted and SDN-based Optical Network Planning with Network-Scale Monitoring Database

Yan, Shihao; Khan, Faisal Nadeem; Mavromatis, Alex; Gkounis, Dimitrios; Fan, Qirui; Ntavou, Foteini; Nikolovgenis, Konstantinos; Meng, Fanchao; Salas, Emilio Hugues; Guo, Changjian; Lu, Chao; Lau, Alan Pak Tao; Nejabati, Reza; Simeonidou, Dimitra

doi:10.1109/ecoc.2017.8346091

Cited by 57 publications

(35 citation statements)

References 3 publications

(6 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…As more advanced neural network structures emerge, CNN is also introduced to monitor the OSNR and modulation format simultaneously [13,58,59]. In [37], ANN is adopted to monitor the OSNR based on the historical data collected from real systems. In [60], principle component analysis (PCA) and ANN are used to monitor the OSNR, bit rate, modulation format, CD and DGD by asynchronous delay-tap plots.…”

Section: Ai-based Qot and Impairment Monitoringmentioning

confidence: 99%

“…Firstly, ML methods are mostly data-driven [32], which means they enable the model to learn the characteristics of the dataset, in principle even without any theoretical information [4,[33][34][35][36]. This specific ability of learning adaptively with data allows ML models to be easily extended to any scenarios if the simulation, experiment or field-trial data for this situation can be obtained [13,23,37]. Secondly, for most optical networks, the number of tunable parameters for link configurations is limited.…”

mentioning

confidence: 99%

See 1 more Smart Citation

AI-Based Modeling and Monitoring Techniques for Future Intelligent Elastic Optical Networks

Liu

Lun

et al. 2020

Applied Sciences

View full text Add to dashboard Cite

With the development of 5G technology, high definition video and internet of things, the capacity demand for optical networks has been increasing dramatically. To fulfill the capacity demand, low-margin optical network is attracting attentions. Therefore, planning tools with higher accuracy are needed and accurate models for quality of transmission (QoT) and impairments are the key elements to achieve this. Moreover, since the margin is low, maintaining the reliability of the optical network is also essential and optical performance monitoring (OPM) is desired. With OPM, controllers can adapt the configuration of the physical layer and detect anomalies. However, considering the heterogeneity of the modern optical network, it is difficult to build such accurate modeling and monitoring tools using traditional analytical methods. Fortunately, data-driven artificial intelligence (AI) provides a promising path. In this paper, we firstly discuss the requirements for adopting AI approaches in optical networks. Then, we review various recent progress of AI-based QoT/impairments modeling and monitoring schemes. We categorize these proposed methods by their functions and summarize advantages and challenges of adopting AI methods for these tasks. We discuss the problems remained for deploying AI-based methods to a practical system and present some possible directions for future investigation.

show abstract

Section: Ai-based Qot and Impairment Monitoringmentioning

confidence: 99%

mentioning

confidence: 99%

AI-Based Modeling and Monitoring Techniques for Future Intelligent Elastic Optical Networks

Liu

Lun

et al. 2020

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Current big-data-analysis techniques enable real-time collection, processing and storage of enormous volumes of ONFM data, as demonstrated in the field trial in [36]. On top of this, ML offers powerful tools to perform OPM thanks to the capability of automatically learning complex mapping between samples or features extracted from the received symbols and channel parameters.…”

Section: A Optical Performance Monitoring (Opm)mentioning

confidence: 99%

A Tutorial on Machine Learning for Failure Management in Optical Networks

Musumeci

Rottondi

Corani

et al. 2019

J. Lightwave Technol.

View full text Add to dashboard Cite

Failure management plays a role of capital importance in optical networks to avoid service disruptions and to satisfy customers' service level agreements. Machine Learning (ML) promises to revolutionize the (mostly manual and humandriven) approaches in which failure management in optical networks has been traditionally managed, by introducing automated methods for failure prediction, detection, localization and identification. This tutorial provides a gentle introduction to some ML techniques that have been recently applied in the field of optical-network failure management. It then introduces a taxonomy to classify failure-management tasks and discusses possible applications of ML for these failure management tasks. Finally, for a reader interested in more implementative details, we provide a step-by-step description of how to solve a representative example of a practical failure-management task.

show abstract

“…In this paper, machine learning technology is explored over a network-scale network configuration and monitoring database (NCMDB) which is implemented and running on a field-trial testbed based on UK national dark fiber facility (NDFIS), with extended works from [3] . The field-trial testbed connects Bristol and Froxfield with a 336.4 km fiber link.…”

Section: Introductionmentioning

confidence: 99%

Field Trial of Machine-Learning-Assisted and SDN-Based Optical Network Management

Yan

Khan

Mavromatis

et al. 2019

Optical Fiber Communication Conference (OFC) 2019

Self Cite

View full text Add to dashboard Cite

show abstract

Field trial of Machine-Learning-assisted and SDN-based Optical Network Planning with Network-Scale Monitoring Database

Cited by 57 publications

References 3 publications

AI-Based Modeling and Monitoring Techniques for Future Intelligent Elastic Optical Networks

AI-Based Modeling and Monitoring Techniques for Future Intelligent Elastic Optical Networks

A Tutorial on Machine Learning for Failure Management in Optical Networks

Field Trial of Machine-Learning-Assisted and SDN-Based Optical Network Management

Contact Info

Product

Resources

About