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Prof. Dr.-Ing. Slawomir Stanczak
- © TU Berlin/Pressestelle/Philipp Arnoldt
Slawomir Stanczak studied electrical engineering with specialization in control theory at the Wroclaw University of Technology and at the Technical University of Berlin (TU Berlin). He received the Dipl.-Ing. degree in 1998 and the Dr.-Ing. degree (summa cum laude) in electrical engineering in 2003, both from TU Berlin; the Habilitation degree (venialegendi) followed in 2006. Since 2015, he has been a Full Professor for network information theory with TU Berlin and the head of the Wireless Communications and Networks department. Prof. Stanczak is a co-author of two books and more than 200 peer-reviewed journal articles and conference papers in the area of information theory, wireless communications, signal processing and machine learning. He was an Associate Editor of the IEEE Transactions on Signal Processing between 2012 and 2015. Since February 2018 Prof. Stanczak has been the chairman of the ITU-T focus group on machine learning for future networks including 5G.
Teaching
- Winter 2020/21
- VL Fundamentals of Digital Wireless Communication (Prof. Dr.-Ing. Slawomir Stanczak)
- VL Mathematical Introduction to Machine Learning (Dr. rer. nat. Igor Bjelakovic)
- VL Introduction to Game Theory with Engineering Applications (Prof. Dr.-Ing. Setareh Maghsudi)
- Summer 2020
- VL Theory and Algorithms of Machine Learning (Prof. Dr.-Ing. Slawomir Stanczak)
- VL Modern Signal Processing and Communications (Dr. Renato L.G. Cavalcante)
- VL Selected Topics in Wireless Communications and Networking (Dr. Zoran Utkovski)
- Winter 2019/20
- VL Fundamentals of Digital Wireless Communication (Prof. Dr.-Ing. Slawomir Stanczak)
- VL Mathematical Introduction to Machine Learning (Dr. rer. nat. Igor Bjelakovic)
- Summer 2019
- VL Theory and Algorithms of Machine Learning (Prof. Dr.-Ing. Slawomir Stanczak)
- VL Modern Signal Processing and Communications (Dr. Renato L.G. Cavalcante)
- VL Selected Topics in Wireless Communications and Networking (Dr. Zoran Utkovski)
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Conference, Symposium, and Workshop Papers
| Citation key | Mehl2020ICC |
|---|---|
| Author | M. Mehlhose, D. A. Awan, R. L.G. Cavalcante, M. Kurras and S. Stanczak |
| Year | 2020 |
| Journal | accepted, IEEE International Conference on Communications (ICC), Dublin, Ireland, 2020 |
| Abstract | Conventional multiuser detection techniques either require a large number of antennas at the receiver for a desired performance, or they are too complex for practical implementation. Moreover, many of these techniques, such as successive interference cancellation (SIC), suffer from errors in parameter estimation (user channels, covariance matrix, noise variance, etc.) that is performed before detection of user data symbols. As an alternative to conventional methods, this paper proposes and demonstrates a low-complexity practical Machine Learning (ML) based receiver that achieves similar (and at times better) performance to the SIC receiver. The proposed receiver does not require parameter estimation; instead it uses supervised learning to detect the user modulation symbols directly. We perform comparisons with minimum mean square error (MMSE) and SIC receivers in terms of symbol error rate (SER) and complexity. |
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Head of Chair
Prof. Dr.-Ing. Slawomir StanczakHFT 400a
Einsteinufer 25
10587 Berlin
Tel.: +49(0)30 314-28465
Fax: +49(0)30 314-28320
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