Published January 12, 2023 | Version v1
Journal article Open

OCATA: a deep-learning-based digital twin for the optical time domain

  • 1. Universitat Politecnica de Catalunya
  • 2. Infinera Portugal
  • 3. Infinera Germany

Description

The development of digital twins to represent the optical transport network might enable multiple applications for network operation, including automation and fault management. In this work, we propose a deep-learning-based digital twin for the optical time domain, named OCATA. OCATA is based on the concatenation of deep neural network (DNN) modeling of optical links and nodes, which facilitates representing lightpaths. The DNNs model linear and nonlinear noise, as well as optical filtering. Additional DNN-based models are proposed to extract useful lightpath metrics, such as lightpath length, number of optical links, and nonlinear fiber parameters. OCATA exhibits low complexity, thus making it ideal for real-time applications. Illustrative results for the application of OCATA to disaggregated and mixed disaggregated-proprietary optical network scenarios reveal remarkable accuracy.

Notes

The research leading to these results has received funding from the MSCA REAL-NET (G.A. 813144), the H2020 B5G-OPEN (G.A. 101016663), and the AEI IBON (PID2020-114135RB-I00) projects and from the ICREA Institution.

Files

[Zenodo] ML-based IQ Digital Twin.pdf

Files (874.4 kB)

Name Size Download all
md5:fa968d3b6ac52f600a4d2d714710b7d6
874.4 kB Preview Download

Additional details

Funding

European Commission
REAL-NET - REAL-time monitoring and mitigation of nonlinear effects in optical NETworks 813144
European Commission
B5G-OPEN - Beyond 5G – OPtical nEtwork coNtinuum 101016663