direkt zum Inhalt springen

direkt zum Hauptnavigationsmenü

Sie sind hier

TU Berlin

Inhalt des Dokuments

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

Novel QoS Control Framework for Automotive Safety-Related and Infotainment Services
Citation key dk2020wcnc
Author D.F. Külzer, S. Stanczak and M. Botsov
Year 2020
ISBN 978-1-7281-3106-1
ISSN 1558-2612
DOI 10.1109/WCNC45663.2020.9120576
Location Seoul, Korea (South), Korea (South)
Journal IEEE Wireless Communications and Networking Conference 2020, May 25-28, Virtual Conference
Month May
Editor IEEE
Abstract Autonomous driving will rely on a multitude of connected applications with stringent quality of service (QoS) requirements in terms of low latency and high reliability. At the same time, passengers relieved of steering duty have the opportunity to enjoy infotainment services that are often associated with high data rates, e.g. video streaming. The simultaneous usage of such safety-related and infotainment services leads to diverse QoS requirements which are difficult to satisfy in current wireless networks. In an effort to address this issue, we propose a two-layer predictive resource allocation framework that leverages the services' properties and incomplete channel information. First, we optimize packet transmission times by a so-called statistical look-ahead scheduling to enhance the network's QoS and spectral efficiency based upon channel distribution information. Second, packets are forwarded to an online scheduler according to the outcome of this first optimization. Physical resources are assigned considering the services' QoS requirements and current channel state. We present a novel heuristic that performs real-time resource assignment. Simulations show that our approach has a potential for improving transmission reliability and spectral efficiency.
Download Bibtex entry

Zusatzinformationen / Extras

Quick Access:

Schnellnavigation zur Seite über Nummerneingabe

Auxiliary Functions

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

Website
Lupe