Cost and power-consumption analysis for power profile monitoring in optical networks

Zhang, Q.; Layec, P.; Morea, A.; Tornatore, M.

doi:10.1049/icp.2023.2454

IET Conf. Proc.

2023

DOI: 10.1049/icp.2023.2454

|View full text |Cite

Cost and power-consumption analysis for power profile monitoring in optical networks

Q. Zhang,

P. Layec,

A. Morea

et al.

Abstract: We quantify and benchmark cost and power consumption of power profile monitoring (PPM) in opaque and transparent IPoWDM networks, comparing it to current optical time-domain reflectometer (OTDR) technology. Results show PPM is preferable even when PPM module adds 80% (50%) cost (power) to one transponder.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Digital-twin-assisted meta learning for soft-failure localization in ROADM-based optical networks

Wang,

Zhang,

et al. 2024

J. Opt. Commun. Netw.

View full text Add to dashboard Cite

Reconfigurable optical add/drop multiplexer (ROADM) nodes are evolving towards high-degree architectures to support growing traffic and enable flexible network connectivity. Due to the complex composition of high-degree ROADMs, soft failures may occur between both inter- and intra-node components, like wavelength selective switches and fiber spans. The intricate ROADM structure significantly contributes to the challenge of localizing inter-/intra-node soft failures in ROADM-based optical networks. Machine learning (ML) has shown to be a promising solution to the problem of soft-failure localization, enabling network operators to take accurate and swift measures to overcome such challenges. However, data scarcity is a main hindrance when using ML for soft-failure localization, especially in the complex scenario of inter- and intra-node soft failures. In this work, we propose a digital-twin-assisted meta-learning framework to localize inter-/intra-node soft failures with limited samples. In our proposed framework, we construct several mirror models using a digital twin of the physical optical network and then generate multiple training tasks. These training tasks serve as pretraining data for the meta learner. Then, we use real data for fine-tuning and testing of the meta learner. The proposed framework is compared with the rule-based reasoning method, transfer-learning-based method, and artificial-neural-network-based method with no pretraining. Experimental results indicate that the proposed framework improves localization accuracy by over 15%, 33%, and 54%, on average, compared to benchmark approaches, respectively.

show abstract

Digital-twin-assisted meta learning for soft-failure localization in ROADM-based optical networks

Wang,

Zhang,

et al. 2024

J. Opt. Commun. Netw.

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Cost and power-consumption analysis for power profile monitoring in optical networks

Cited by 1 publication

References 11 publications

Digital-twin-assisted meta learning for soft-failure localization in ROADM-based optical networks

Digital-twin-assisted meta learning for soft-failure localization in ROADM-based optical networks

Contact Info

Product

Resources

About