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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)
- 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)
- 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 | agosIcassp2020 |
|---|---|
| Author | P. Agostini, Z. Utkovski and S. Stanczak |
| Year | 2020 |
| ISBN | 978-1-5090-6631-5 |
| ISSN | 2379-190X |
| Location | Barcelona, Spain |
| Journal | ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 4-8 May 2020 in Barcelona, Spain |
| Month | May |
| Editor | IEEE |
| Organization | 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) |
| Abstract | Channel charting (CC) is an emerging machine learning framework that aims at learning lower-dimensional representations of the radio geometry from collected channel state information (CSI) in an area of interest, such that spatial relations of the representations in the different domains are preserved. Extracting features capable of correctly representing spatial properties between positions is crucial for learning reliable channel charts. Most approaches to CC in the literature rely on range distance estimates, which have the drawback that they only provide accurate distance information for colinear positions. Distances between positions with large azimuth separation are constantly underestimated using these approaches, and thus incorrectly mapped to close neighborhoods. In this paper, we introduce a correlation matrix distance (CMD) based dissimilarity measure for CC that allows us to group CSI measurements according to their co-linearity. This provides us with the capability to discard points for which large distance errors are made, and to build a neighborhood graph between approximately collinear positions. The neighborhood graph allows us to state the problem of CC as an instance of an Euclidean distance matrix completion (EDMC) problem where side-information can be naturally introduced via convex box-constraints. |
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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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