Boston Chapter of the IEEE Photonics Society

Abstract:  Rapid improvements in machine learning over the past decade are beginning to have far-reaching effects. In this work, we propose a new machine-learning approach for fiber-optic systems in which signal propagation is governed by the nonlinear Schrödinger equation (NLSE). Our main idea is to exploit the fact that the popular split-step method for numerically solving the NLSE has essentially the same functional form as a “deep” multi-layer neural network; in both cases, one alternates linear steps and pointwise nonlinearities. We demonstrate that this connection allows for a principled machine-learning approach by appropriately parameterizing the split-step method and viewing the linear steps as general linear functions, similar to the weight matrices in a neural network. The resulting physics-based machine-learning model has several key advantages compared to conventional “black-box” function approximators. For example, it allows us to easily examine and interpret the learned solutions in order to understand why they perform well.

Biography:  Christian Häger is a researcher in the Communication Systems research group. He received the Dipl.-Ing. degree (M.Sc. equivalent) in electrical engineering from Ulm University, Germany, in 2011 and his Ph.D. degree in communication theory from Chalmers University of Technology, Sweden, in 2016. From 2016 until 2019, he was a postdoctoral researcher at the Department of Electrical and Computer Engineering at Duke University, USA. Since 2017, he is a postdoctoral researcher at the Department of Electrical Engineering at Chalmers University of Technology. His research interests include modern coding theory, fiber-optic communications, and machine learning. He received the Marie Sklodowska-Curie Global Fellowship from the European Commission in 2017