Skip to main navigation Skip to search Skip to main content

MAGC-RSA: Multi-Agent Graph Convolutional Reinforcement Learning for Distributed Routing and Spectrum Assignment in Elastic Optical Networks

  • Huy Tran Quang
  • , Omar Houidi
  • , Javier Errea-Moreno
  • , Dominique Verchere
  • , Djamal Zeghlache
  • Bell Labs
  • CNRS UMR 5157 SAMOVAR

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper proposes MAGC-RSA, a Multi-Agent Graph Convolutional Reinforcement Learning approach, to solve the Routing and Spectrum Assignment (RSA) problem in a distributed manner. A blocking probability reduction of 80% can be achieved compared to the Shortest Path First-Fit approach.

Original languageEnglish
Title of host publication2022 European Conference on Optical Communication, ECOC 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781957171159
Publication statusPublished - 1 Jan 2022
Event2022 European Conference on Optical Communication, ECOC 2022 - Basel, Switzerland
Duration: 18 Sept 202222 Sept 2022

Publication series

Name2022 European Conference on Optical Communication, ECOC 2022

Conference

Conference2022 European Conference on Optical Communication, ECOC 2022
Country/TerritorySwitzerland
CityBasel
Period18/09/2222/09/22

Fingerprint

Dive into the research topics of 'MAGC-RSA: Multi-Agent Graph Convolutional Reinforcement Learning for Distributed Routing and Spectrum Assignment in Elastic Optical Networks'. Together they form a unique fingerprint.

Cite this