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Prof. Dr.-Ing. Slawomir Stanczak


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.     


  • 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





Towards Secure Over-The-Air Computation
Citation key MatSecu2020
Author M. Frey, I. Bjelakovic and S. Stanczak
Year 2020
Journal Submitted to Problems of Information Transmission. Preprint available at arXiv:2001.03174
Abstract We revisit the problem of distributed approximation of functions over multiple-access channels. Contrary to previous works, however, we do not consider the approximation problem itself, but instead we propose a method of incorporating security constraints into a class of approximation schemes to protect against passive eavesdropping. We specifically consider a scenario in which the jamming signal is stronger for the legitimate receiver than it is for the eavesdropper, and we show that in this case jamming techniques are feasible in the sense that they can deteriorate the eavesdropper's signal while not affecting the usefulness of the legitimate receiver's signal. Key ingredients for our scheme are channel resolvability as well as a newly proven result for coding for compound channels with continuous alphabets which is more general than similar results from prior works and may thus be of independent interest.
Download Bibtex entry


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

D.F. Külzer, F. Debbichi, S. Stanczak and M. Botsov (2021). On Latency Prediction with Deep Learning and Passive Probing at High Mobility. IEEE International Conference on Communications (ICC) 2021, Montreal, Canada (virtual conference), in June 14-23, 2021

D. Schäufele, M. Kasparick, J. Schwardmann, J. Morgenroth and S. Stanczak (2021). Terminal-Side Data Rate Prediction For High-Mobility Users. IEEE 93rd Vehicular Technology Conference (VTC2021-Spring), Helsinki, Finland, April 2021

M. Frey, I. Bjelakovic and S. Stanczak (2021). Over-The-Air Computation in Correlated Channels. Accepted for publication at IEEE 2020 Information Theory Workshop (ITW), April 11-15, 2021, final version on arXiv:2101.04690

D.F. Külzer, M. Kasparick, A. Palaios, R. Sattiraju, O. D. Ramos-Cantor, D. Wieruch, H. Tchouankem, F. Göttsch, P. Geuer, J. Schwardmann, G. Fettweis, H.D. Schotten and S. Stanczak (2021). AI4Mobile: Use Cases and Challenges of AI-based QoS Prediction for High-Mobility Scenarios. IEEE Vehicular Technology Conference (VTC Spring) 2021, April 25-28, in Helsinki, Finland

A. Pfadler, P. Jung, T. Szollmann and S. Stanczak (2021). Pulse-Shaped OTFS over Doubly-Dispersive Channels: One-Tap vs. Full LMMSE Equalizers. IEEE International Conference on Communications, 14-23 June 2021, Montreal, Canada

Bezmenov, Maria and Utkovski, Zoran and Sambale, Klaus and Stanczak, Slawomir (2021). Semi-Persistent Scheduling with Single Shot Transmissions for Aperiodic Traffic. 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring), 1–7.

Dai, Sida and Kurras, Martin and Thiele, Lars and Stanczak, Slawomir and Chen, Litao and Zhong, Zhimeng (2021). Deep Learning for Massive MIMO: Channel Completion for TDD Downlink. 2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), 696–702.

O. Taghizadeh, S. Stanczak, H. Iimori and G. T. Freitas de Abreu (2020). Full-Duplex AF MIMO Relaying: Impairments Aware Design and Performance Analysis. 2020 IEEE Global Communications Conference: Signal Processing for Communications (Globecom2020 SPC), December 7 - 11, in Taipei, Taiwan

A. Pfadler, P. Jung and S. Stanczak (2020). Mobility Modes for Pulse-Shaped OTFS with Linear Equalizer. IEEE Globecom 2020, December 7-11, Taipei, Taiwan

P. Agostini, Z. Utkovski and S. Stanczak (2020). Channel Charting: an Euclidean Distance Matrix Completion Perspective. ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 4-8 May 2020 in Barcelona, Spain

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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