direkt zum Inhalt springen

direkt zum Hauptnavigationsmenü

Sie sind hier

TU Berlin

Inhalt des Dokuments

Es gibt keine deutsche Übersetzung dieser Webseite.

Prof. Dr.-Ing. Slawomir Stanczak

Lupe

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)

 

You can also find me on:

Fraunhofer Heinrich-Hertz-Institut

Google Scholar

arXiv

LinkedIn


Publications

Preprints

M. Frey, I. Bjelakovic and S. Stanczak (2021). Over-The-Air Computation in Correlated Channels. Submitted to IEEE Transactions on Signal Processing. Final version available at arXiv:2101.04690


M. Frey, I. Bjelakovic and S. Stanczak (2020). Towards Secure Over-The-Air Computation. Submitted to IEEE Transactions on Information Forensics and Security. Preprint available at arXiv:2001.03174


Books

S. Stanczak, M. Wiczanowski and H. Boche (2009). Fundamentals of Resource Allocation in Wireless Networks. volume 3 of Foundations in Signal Processing, Communications and Networking. Springer, Berlin, 2009. Springer, Berlin.


S. Stanczak, M. Wiczanowski and H. Boche (2006). Resource Allocation in Wireless Networks - Theory and Algorithms. Lecture Notes in Computer Science (LNCS 4000). Springer, Berlin, 2006. Springer, Berlin.


Book Chapters

D. A. Awan, R.L.G. Cavalcante, M. Yukawa and S. Stanczak (2020). Adaptive Learning for Symbol Detection. Machine Learning for Future Wireless Communications. Wiley & IEEE Press, 15.


S. Maghsudi and S. Stanczak (2015). Communications in Interference-Limited Networks. chapter Distributed Channel Selection for Underlay Device-to-Device Communications: A Game- Theoretical Learning Framework. Springer International Publishing, 2015. Springer International Publishing.


M. Goldenbaum, S. Stanczak and H. Boche (2015). Communications in Interference-Limited Networks. chapter Interference-Aware Analog Computation over the Wireless Channel: Fundamentals and Strategies. Springer International Publishing, 2015. Springer International Publishing.


R. L. G. Cavalcante, S. Stanczak and I. Yamada (2014). Cooperative Cognitive Radios with Diffusion Networks. chapter Cognitive Radio and Sharing Unlicensed Spectrum in the book Mechanisms and Games for Dynamic Spectrum Allocation, Cambridge University Press, UK, 2014, 262-303.


S. Stanczak and H. Boche (2005). Towards a better understanding of the QoS tradeoff in multiuser multiple antenna systems. Smart Antennas–State-of-the-Art. Hindawi Publishing Corporation, 521-543.


Journal Publications

M. A. Gutierrez-Estevez, M. Kasparick and S. Stanczak (2021). Online Learning of Any-to-Any Path Loss Maps. IEEE Communications Letters


J. Dommel, Z. Utkovski, O. Simeone and S. Stanczak (2021). Joint Source-Channel Coding for Semantics-Aware Grant-Free Radio Access in IoT Fog Networks. IEEE Signal Processing Letters


F. Molinari, N. Agrawal, S. Stanczak and J. Raisch (2021). Max-Consensus Over Fading Wireless Channels. IEEE Transactions on Control of Network Systems, Jan. 2021


D. A. Awan, R. L.G. Cavalcante and S. Stanczak (2020). Robust Cell-Load Learning with a Small Sample Set. IEEE Transactions on Signal Processing (TSP), 68:270-283.


R. Hernangómez, A. Santra and S. Stanczak (2020). A Study on Feature Processing Schemes for Deep-Learning-Based Human Activity Classification Using Frequency-Modulated Continuous-Wave Radar. IET Radar, Sonar & Navigation, Volume 14, Issue 7, July 2020, 10 pp.


C.- X. Wang, M. Di Renzo, S. Stanczak, S. Wang and E. G. Larsson (2020). Artificial Intelligence Enabled Wireless Networking for 5G and Beyond: Recent Advances and Future Challenges. IEEE Wireless Communications (Volume 27, Issue: 1, pp. 16-23, Feb.


G. Bräutigam, R. L.G. Cavalcante, M. Kasparick, A. Keller and S. Stanczak (2020). AI and open interfaces: Key enablers for campus networks. ITU News Magazine - AI and Machine Learning in 5G, no. 5, p. 55, open access, Dec.


R. L.G. Cavalcante, Q. Liao and S. Stanczak (2019). Connections between spectral properties of asymptotic mappings and solutions to wireless network problems. IEEE Transactions on Signal Processing, Feb. 2019


S. Limmer and S. Stanczak (2018). A Neural Architecture for Bayesian Compressive Sensing over the Simplex via Laplace Techniques. IEEE Trans. on Signal Processing, 66(22):6002-6015, Nov. 2018.


C. Bockelmann, N. Pratas, G. Wunder, S. Saur, M. Navorro, D. Gregoratti, G. Vivier, E. de Carvalho, Y. Ji, C. Stefanovic, P. Popovski, Q. Wang, M. Schellmann, E. Kosmatos, P. Demestichas, M. Raceala-Motoc, P. Jung, S. Stanczak and A. Dekorsy (2018). Towards Massive Connectivity Support for Scalable mMTC Communications in 5G networks. IEEE Access (Volume: 6), pages 28969 - 28992, May 16, 2018


Conference, Symposium, and Workshop Papers

Channel Charting: an Euclidean Distance Matrix Completion Perspective
Zitatschlüssel agosIcassp2020
Autor P. Agostini, Z. Utkovski and S. Stanczak
Jahr 2020
ISBN 978-1-5090-6631-5
ISSN 2379-190X
Ort Barcelona, Spain
Journal ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 4-8 May 2020 in Barcelona, Spain
Monat May
Herausgeber IEEE
Organisation 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Zusammenfassung 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.
Download Bibtex Eintrag

Zusatzinformationen / Extras

Direktzugang:

Schnellnavigation zur Seite über Nummerneingabe

This site uses Matomo for anonymized web analytics. More information and opt-out options under data protection.

Head of Chair

Prof. Dr.-Ing. Slawomir Stanczak
HFT 400a
Einsteinufer 25
10587 Berlin
Tel.: +49(0)30 314-28465
Fax: +49(0)30 314-28320

Webseite
Lupe