M.Sc. Alex Jäger
Technische Universität München
Lehrstuhl für Nachrichtentechnik (Prof. Kramer)
Postadresse
Postal:
Theresienstr. 90
80333 München
Biografie
I received my B.Eng. degree from the University of Applied Sciences Konstanz (HTWG) in 2020, and subsequently obtained my M.Sc. degree from the Technical University of Munich (TUM) in 2022.
During my Masters thesis, I conducted research on the topic of achievable information rates for space-division multiplexed short-reach fiber-optic communication systems using direct-detection receivers, while staying with the Optical Networks Group (ONG) at University College London (UCL).
Forschung
Due to the presence of Kerr nonlinearities, a closed-form expression for the input-output relation of the fiber-optic channel cannot be derived, rendering the channel capacity indeterminate. Digital signal processing techniques, such as digital back-propagation, have been proposed as a means of mitigating nonlinear and linear fiber effects.
However, in wavelength switched networks, where receivers are only able to access their specific channel of interest and not interfering channels, only intra-channel effects, such as self-phase modulation, can be mitigated. As a result, cross-phase modulation remains a significant source of distortion in the received signal.
The nonlinear distortions that remain are typically modeled as correlated phase noise and inter-symbol interference within the channel of interest. My research focuses on developing coding schemes and receivers capable of mitigating these distortions.
Lehre
Digital Signal Processing for Optical Communication Systems (ST 2023)
Machine Learning for Communications (WT 2023/24)
Angebotene Abschlussarbeiten
Laufende Abschlussarbeiten
Spiral Constellations for Long-Haul Fiber-Optic Links
Beschreibung
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Betreuer:
Neural Network-Based Compensation Techniques for Nonlinear Distortions in Fibre-Optic Links
Beschreibung
Optical signals experience nonlinear distortions - so called Kerr nonlinearities - when propagating through a fiber. This makes signal processing more complicated than in linear systems. Multiple ways to cope with this have been proposed in the past, including, e.g., digital backpropagation (DBP), Volterra equalizers or phase conjugation (see [1] for a good overview of conventional methods). Unfortunately, these methods are often unfeasible.
Due to their inherent nonlinear characteristics, neural networks might be a suitable solution for compensating Kerr nonlinearities. They might be used, e.g, as a demapper [2] or as a more efficient alternative to DBP [3]. A good overview of usage of neural networks for nonlinearity compensation is given in [4, Sec. Vb] . This paper also provides a good introduction to machine learning techniques relevant for optical communications.
In this seminar, the student is expected to research neural network based compensation techniques for fibre nonlinearities (e.g., one of the ones briefly described in [4, Sec. Vb]) and present either one in detail or multiple ones as an overview.
Voraussetzungen
Basic knowledge of optical communications (Optical Communication Systems EI5075, Digital Signal Processing for Optical Communication Systems EI71067 or comparable) is required. Some experience with machine learning is helpful, but papers like [4] can alternatively provide a good introduction to machine learning, as well.
If you are interested, feel free to just contact me via email. We can then discuss the details and fit the topic to your interests.
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[1] A. Amari, O. A. Dobre, R. Venkatesan, O. S. S. Kumar, P. Ciblat and Y. Jaouën, "A Survey on Fiber Nonlinearity Compensation for 400 Gb/s and Beyond Optical Communication Systems," in IEEE Communications Surveys & Tutorials, vol. 19, no. 4, pp. 3097-3113, Fourthquarter 2017, doi: 10.1109/COMST.2017.2719958.
[2] M. Schaedler, S. Calabrò, F. Pittalà, C. Bluemm, M. Kuschnerov and S. Pachnicke, "Neural Network-Based Soft-Demapping for Nonlinear Channels," 2020 Optical Fiber Communications Conference and Exhibition (OFC), San Diego, CA, USA, 2020, pp. 1-3.
[3] C. Häger and H. D. Pfister, "Nonlinear Interference Mitigation via Deep Neural Networks," 2018 Optical Fiber Communications Conference and Exposition (OFC), San Diego, CA, USA, 2018, pp. 1-3.
[4] F. N. Khan, Q. Fan, C. Lu and A. P. T. Lau, "An Optical Communication's Perspective on Machine Learning and Its Applications," in Journal of Lightwave Technology, vol. 37, no. 2, pp. 493-516, 15 Jan.15, 2019, doi: 10.1109/JLT.2019.2897313.