The Optical Networking and Communication
Conference & Exhibition

Moscone Center,
San Francisco, California, USA

The Role of Machine Learning for the Next-generation of Optical Communication Systems and Networks​

Monday, March 9, 2020
2:00 PM - 6:30 PM

Room number: 6C


Darko Zibar, Technical University of Denmark, DTU Fotonik, Denmark; Alan Pak Tao Lau; The Hong Kong Polytechnic University, Hong Kong; Jelena Pesic, Nokia Bell Labs, France


The field of machine learning provides a new set of tools to the optical communication community. These tools are valuable when the relationship between the input and the output of the system is highly complex or when there is a lack of analytical models describing the relation between the input and the output of the system.

An example of such a situation is when communicating over the fiber-optic channel, which is inherently nonlinear and highly complex.  As such, optimal transmitter and receiver architectures that maximize the capacity—distance product of fiber optic channel are still unknown. Moreover, to satisfy future capacity demands, next generation optical communications may employ a combination of ultra-wideband transmission (O+S+E+C+L) and spatial-division multiplexing. Those systems will be highly complex, and bringing the tools from the machine learning community to design transmitter, and receiver architecture as well as optical amplification schemes may prove very beneficial. Finally, performing network monitoring and optimization for improved network efficiency is rather challenging due to many transmission parameters involved.

The goal of this symposium is to gather active groups working on this emerging topic and chart the future for the application of machine learning in optical communication systems and networks.   


Group A    
Keisuke Kojima; Mitsubishi Electric Research Laboratories, USA    
Deep Learning for Inverse Design of Optical Device 

Maxim Kuschnerov; Huawei, Germany    
Advances in Deep Learning for Digital Signal Processing in Coherent Optical Modems

Josh Gordon; NIST, USA    
Summary: NIST Workshop on Machine Learning for Optical Communication Systems
Group B    
Cristina Rottondi; Politecnico di Torino, Italy    
Active vs Transfer Learning for QoT Estimation with Low Number of Probes in Optical Networks

Andrew Shiner; Ciena, Canada    
Neural Network Training for OSNR Estimation - from Prototype to Product

Åsa Ribbe; EPO, Germany    
Towards Intelligent Optical Networks: The Role of Intellectual Property

Marija Furdek; Chalmers University of Technology, Sweden    
Machine Learning for Optical Network Security Management

Sponsored by: