Physics-Based Deep Learning for Optical Data Transmission and Distributed Sensing
Research Project , 2021 – 2024

In order to cope with ever-increasing data traffic demands, recent years have witnessed an explosion of research interest in replacing handcrafted communication algorithms with machine-learning-based artificial intelligence (AI). However, prior work has largely resorted to massively overparameterized neural networks, resulting in unrealistic hardware requirements and unsatisfactory black-box solutions. This project will instead explore a new approach, specifically for optical fiber systems, that will not only lead to improved transmission performance, but also transform already-deployed communication fibers into intelligent sensors providing real-time information about various physical effects along the propagation path, e.g., mechanical stresses or vibrations. Our approach leverages well-established physical models and principles as a foundation, in particular the nonlinear differential equations governing the propagation dynamics. The proposed 4-year project is divided into four objectives/tasks: (i) receiver-side machine-learning models, (ii) transmitter-side signal shapers, (iii) data-driven optimization, and (iv) trade-offs between communication and sensing. The data used for training the developed solutions will originate from simulations supplemented by lab transmission experiments. Our target is to develop new AI-based solutions that combine, for the first time, high-speed optical data transmission and distributed sensing capabilities into a single DSP platform.

Participants

Christian Häger (contact)

Assistant Professor at Chalmers, Electrical Engineering, Communication and Antenna Systems, Communication Systems

Funding

Swedish Research Council (VR)

Funding Chalmers participation during 2021–2024

More information

Latest update

2021-03-29