QoT Estimation for Large-scale Mixed-rate Disaggregated Metro DCI Networks by Artificial Neural Networks

Yan He, Kausthubh Chandramouli, Zhiqun Zhai, Sai Chen, Liang Dou, Chongjin Xie, Chao Lu, Alan Pak Tao Lau

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

We proposed an artificial neural network (ANN)-based QoT estimator for large-scale mixed-rate disaggregated metro DCI networks with an estimation error standard deviation of 0.3 dB, outperforming analytical-based methods with vendor-specific transponder SNR characterization.

Original languageEnglish
Title of host publication2024 Optical Fiber Communications Conference and Exhibition, OFC 2024 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-3
Number of pages3
ISBN (Electronic)9781957171326
DOIs
Publication statusPublished - Mar 2024
Event2024 Optical Fiber Communications Conference and Exhibition, OFC 2024 - San Diego, United States
Duration: 24 Mar 202428 Mar 2024

Publication series

Name2024 Optical Fiber Communications Conference and Exhibition, OFC 2024 - Proceedings

Conference

Conference2024 Optical Fiber Communications Conference and Exhibition, OFC 2024
Country/TerritoryUnited States
CitySan Diego
Period24/03/2428/03/24

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing
  • Artificial Intelligence
  • Electrical and Electronic Engineering
  • Instrumentation
  • Atomic and Molecular Physics, and Optics

Fingerprint

Dive into the research topics of 'QoT Estimation for Large-scale Mixed-rate Disaggregated Metro DCI Networks by Artificial Neural Networks'. Together they form a unique fingerprint.

Cite this