Reinforcement Learning for Generalized Parameter Optimization in Elastic Optical Networks

Elastic Optical Networking enables efficient use of spectral resources at the cost of a large parameter space which needs to be optimized to maximize transmission bandwidth. By formulating this optimization problem as a Markov Decision Process, we show that by using a state of the art Reinforcement...

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Bibliographic Details
Published inJournal of lightwave technology Vol. 40; no. 3; pp. 567 - 574
Main Authors Koch, Rebekka, Kuhl, Sebastian, Morais, Rui Manuel, Spinnler, Bernhard, Schairer, Wolfgang, Sommernkorn-Krombholz, Bernd, Pachnicke, Stephan
Format Journal Article
LanguageEnglish
Published New York IEEE 01.02.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Advantage actor-critic algorithm
Algorithms
deep reinforcement learning
elastic optical networks (EONs)
Genetic algorithms
Machine learning
Markov processes
modulation and spectrum assignment (RMSA)
Optical communication
Optical fiber amplifiers
Optical fiber dispersion
Optical fiber networks
Optimization
Parameters
routing
Sociology
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Summary:Elastic Optical Networking enables efficient use of spectral resources at the cost of a large parameter space which needs to be optimized to maximize transmission bandwidth. By formulating this optimization problem as a Markov Decision Process, we show that by using a state of the art Reinforcement Learning algorithm an agent can be trained, which is able to select near optimal parameters for different link conditions within seconds. Furthermore, the trained agent is able to generalize to unseen conditions, removing the need to optimize and train for every possible link scenario.
ISSN:0733-8724
1558-2213
DOI:10.1109/JLT.2021.3123271