Fiber optical networks have been widely deployed at different scales to form the core infrastructure of today's Internet backbone, telecommunication networks, and smart connected communities. These networks can deliver high bandwidth data services at deterministic low latency leveraging advanced techniques and hardware such as wavelength-division multiplexing technique and reconfigurable add-drop multiplexer units.

Accurate modeling and performance estimation, as well as autonomous adaption and reconfiguration of optical links are essential for optical system designs, particularly due to wavelength-dependent gain spectrum of optical amplifiers and fiber nonlinearity. Moreover, each fiber-optic cable can also serve as a high-resolution sensor since it is sensitive to different environmental effects (e.g., vibration and temperature) due to the linear and nonlinear light scattering.

In this webinar, Tingjun Chen will first present the modeling of erbium-doped fiber amplifiers using machine learning and its application in signal quality estimation in multi-span ROADM systems. Dr. Chen will then present his investigation on the coexistence of heterogeneous communication, fiber sensing, and radio-over-fiber signals that co-propagate on the same fiber, focusing on their impact on key performance metrics including bit error rate and sensing resolution. Lastly, Dr. Chen will highlight a series of measurements and field trials that support his research, conducted on the PAWR COSMO platforms in NYC and NC.

Subject Matter Level: Intermediate - Assumes basic knowledge of the topic

What You Will Learn:
• Performance modeling and QoT prediction in optical networks
• Co-existence of fiber-optic communication and sensing systems
• Large-scale testbeds and filed trials

Who Should Attend:
• Researchers from both academic and industry working on above mentioned topics

About the Presenter: Tingjun Chen from Duke University

Tingjun Chen received the Ph.D. degree in Electrical Engineering from Columbia University in 2020, and the B.Eng. degree in Electronic Engineering from Tsinghua University in 2014. Between 2020–2021 he was a Postdoctoral Associate at Yale University. Since Fall 2021 he has been with Duke University where he is an Assistant Professor in the Departments of Electrical & Computer Engineering and Computer Science (secondary appointment). His research interests are in the area of networking and communications with a specific focus on next-generation wireless, optical, mobile networks, as well as Internet-of-Things (IoT) systems. Tingjun received the IBM Academic Award, the Google Research Scholar Award, the Columbia Engineering Morton B. Friedman Memorial Prize for Excellence, the Columbia University Eli Jury Award, and the Facebook Fellowship. He is also a co-recipient of several paper awards, including the ACM CoNEXT’16 Best Paper Award, ECOC’23 Best Paper Award.