Machine Learning For Optical Communication Systems

NIST will hold a workshop at the Boulder Colorado Laboratories to discuss the role of machine learning (ML) in optical communication systems. Optical communication systems are increasingly used closer to the network edge and are expected to find use in new applications that require more intelligent 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. Furthermore, optical systems are running up against spectral density limits that threaten traditional capacity-based scaling. New efficiency-based scaling methods are needed to further improve the cost/bit/s without relying on capacity improvements alone. Artificial intelligence (AI) and machine learning provide a new direction with the potential to both enable wider use of software controls and to further optimize the efficiency of optical systems across multiple dimensions. Reference data sets for ML would improve functionality and operability across industry further enabling scaling and efficiency. This workshop seeks to identify and develop applications of AI, and ML in the context of accelerating the use of software-based networking in optical systems for improved performance and scalability. Paths to realizing reference training data sets for ML in optical communications systems including needs for new or different metrology will be examined. Workshop outcomes include a white paper for a plan and path to develop and disseminate reference data sets for ML training and applications to open and programmable optical communication systems, as well as a working group to further develop these ideas.

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