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Accurate Closed-Form Real-Time EGN Model Formula Leveraging Machine-Learning Over 8500 Thoroughly Randomized Full C-Band Systems

We derived an approximate non-linear interference (NLI) closed-form model (CFM), capable of handling a very broad range of optical WDM system scenarios. We tested the CFM over 8500 randomized C-band WDM systems, of which 6250 were fully-loaded and 2250 were partially loaded. The systems had highly diversified channel formats, symbol rates, fibers, as well as other parameters. We improved the CFM accuracy by augmenting the formula with simple machine-learning factors, optimized by leveraging the system test-set. We further improved the CFM by adding a term which models special situations where NLI has high self-coherence. In the end, we obtained a very good match with the results found using the numerically-integrated Enhanced GN-model (or EGN-model). We also checked the CFM accuracy by comparing its predictions with full-C-Band split-step simulations of 300 randomized systems. The combined high accuracy and very fast computation time (milliseconds) of the CFM potentially make it an effective tool for real-time physical-layer-aware optical network management and control.

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Judul Seri

JOURNAL OF LIGHTWAVE TECHNOLOGY; Vol.38 No.18 September 15 2020

No. Panggil

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Penerbit

: .,

Deskripsi Fisik

p. 4987 - 4999

Bahasa

English

ISBN/ISSN

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Klasifikasi

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Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

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Subjek

WDM NETWORKS
CONTROL PLANE
GN-MODEL
COHERENT TRANSMISSION
BIG-DATA
NON-LINEARITY
MACHINE-LEARNING
NLI
EGN-MODEL
PHYSICAL LAYER AWARENESS

Info Detail Spesifik

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Pernyataan Tanggungjawab

Mahdi Ranjbar Zefreh, Fabrizio Forghieri, Stefano Piciaccia, Pierluigi Poggiolini

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