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Summary: Workshop on Machine Learning for Optical Communication Systems



Joshua A. Gordon, Abdella Battou, Michael P. Majurski, Dan Kilper, Uiara Celine, Massimo Tonatore, Joao Pedro, Jesse Simsarian, Jim Westdorp, Darko Zibar


Optical communication systems are expected to find use in new applications that require more intelligent and automated functionality. Optical networks are needed to address the high speeds and low latency of 5G wireless networks. The analog nature of optical transmission and the complexity of operation and management remain an impediment to greater use of software controls. The optical community at large has proposed many possible applications and avenues for using and implementing artificial intelligence and machine learning to improve functionality of optical systems for communications. However, broad agreement has yet to be reached due to both technical and non-technical reasons. On August 2nd , 2019 The National Institute of Standards and Technology (NIST) Communications Technology Laboratory (CTL) hosted a Workshop on Machine Learning for Optical Communication Systems to bring together industry, academia and government in order to discuss the roll of AI and ML in optical communication systems. This document provides an overview and summary of the workshop.
Special Publication (NIST SP) - 2100-04
Report Number


Artificial Intelligence, Dissagregation, Machine Learning, Network Defragmentation Open Line, Optical Networks, ROADM, Transponder, Quality of Transmission, QoT


Gordon, J. , Battou, A. , Majurski, M. , Kilper, D. , Celine, U. , Tonatore, M. , Pedro, J. , Simsarian, J. , Westdorp, J. and Zibar, D. (2020), Summary: Workshop on Machine Learning for Optical Communication Systems, Special Publication (NIST SP), National Institute of Standards and Technology, Gaithersburg, MD, [online], (Accessed July 13, 2024)


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Created March 26, 2020, Updated May 4, 2021