Dr.-Ing. Tobias Fehenberger
Associate Professorship of Line Transmission Technology (Prof. Hanik)
- Dr.-Ing. (summa cum laude), Technical University of Munich (TUM), 2017
- B.Sc. in Management and Technology, TUM, 2017
- Dipl.-Ing. in Electrical Engineering and Information Technology, TUM, 2011
- Semester abroad at the American University Beirut, Lebanon, and University College London, UK
For more information about me, please visit www.fehenberger.de
In my research, I investigate optical communication systems, which have become the backbone of the digital age. Virtually all IP-based traffic of the Internet is transmitted over optical fibers, enabling high data rate services such as HD video streaming and cloud storage. The low loss of optical fibers over a huge spectrum of several THz and optical wideband amplifiers are key technologies that enable the transmission of several trillion bits (Terabits) per second over thousands of kilometers of single-mode optical fiber with a core that is thinner than a human hair. Although these data rates are without any doubt huge, a steady demand for increased throughput has been observed for the past decades, with no end in sight. High-order modulation formats and advanced forward error correction schemes are potential options to achieve larger data rates. My research addresses these two entities jointly, a field known as coded modulation.
In particular, I examine methods to determine the maximum throughput, measured in bits per channel use, for optical communication systems. These achievable information rates allow to immediately link any changes made to the fiber optical channel to an increase (or decrease) in data rate. I also investigate probabilistic constellation shaping where symbols have unequal probability of occurrence (see figure below). This power-efficient technique can improve the performance of many optical communication systems, yet leads to some unexpected and interesting effects for the nonlinear fiber channel - for example, the nonlinear interference is increased by probabilistic shaping.