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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 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)
- Winter 2018/19
- VL Fundamentals of Digital Wireless Communication (Prof. Dr.-Ing. Slawomir Stanczak)
- VL Mathematical Introduction to Machine Learning (Dr. rer. nat. Igor Bjelakovic)
- Summer 2018
- VL Mathematical Methods in Signal Processing and Communications (Prof. Dr.-Ing. S.Stanczak)
- VL Modern Signal Processing for Communications (Dr. R. Cavalcante)
- Winter 2017/18
- VL Fundamentals of Digital Wireless Communication
- VL Selected topics in Wireless Network Optimization
- Summer 2017
- VL Mathematical Methods in Signal Processing and Communications
- VL Modern Signal Processing for Communications
- Winter 2016/17
- VL Fundamentals of Digital Wireless Communication
- VL Selected topics in Wireless Network Optimization
- Summer 2016
- VL Wireless Network Analysis and Optimization
- VL Modern Signal Processing for Communications
- Winter 2015/16
- VL Mathematical Methods in Signal Processing and Communications
- SE Selected Topics in Wireless Network Optimization
- Summer 2015
- VL Cross-Layer Optimization and Resource Allocation in Wireless Networks
- Winter 2014/15
- VL Foundations of Digital and Wireless Communication
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Conference, Symposium, and Workshop Papers
| Citation key | Ren2020SSP |
|---|---|
| Author | R. L. G. Cavalcante and S. Stanczak |
| Year | 2020 |
| Journal | IEEE Statistical Signal Processing Workshop 2020, Rio de Janeiro, Brazil, 12th-15th July, 2020 (to appear) |
| Abstract | Information about the angular power spectra (APS) of signals impinging on antenna arrays have important applications in massive MIMO systems such as user clustering, angle-of-arrival estimation, and channel covariance estimation in FDD systems, to cite a few. Current approaches for the estimation of APS can be divided into two main groups: model based methods and data driven methods. The former methods are able to produce reliable estimates with little side information and no training , but they do not exploit any information from datasets (if available) to improve the estimates. In contrast, pure data-driven methods can provided good performance without any knowledge about physical models, but they do not provide any guarantees of robustness against sudden changes in the propagation environment, a common occurrence in wireless systems. Against this background, we propose novel hybrid model and data driven algorithms that use both information about models and any available dataset. To this end, we modify projection and optimization methods for APS estimation by using an inner product (and hence the induced norm and metric) that is learned from data. The proposed algorithms are able to produce reliable estimates that exploit both statistical information available in datasets and model knowledge. As a result, they are able to provide robustness against relatively large changes in the propagation environment without the need to perform frequent training. |
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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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