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Prof. Dr.-Ing. Slawomir Stanczak

Lupe [1]
© 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 at Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute (HHI). Prof. Stanczak has been involved in research and development activities in wireless communications since 1997. In 2004 and 2007, he was a Visiting Professor with RWTH Aachen University and in 2008, he was a Visiting Scientist with Stanford University, Stanford, CA, USA. He 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. Prof. Stanczak received research fellowships from the German Research Foundation and the Best Paper Award from the German Communication Engineering Society in 2014. He was a Co-chair of the 14th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC 2013). Between 2009 and 2011, he was an Associate Editor of the European Transactions for Telecommunications (information theory) and an Associate Editor of the IEEE Transactions on Signal Processing between 2012 - 2015 and the chair of the ITU-T Focus Group on Machine Learning for Future Networks including 5G from 2017 - 2020. Since 2020, Prof. Stanczak is chairman of the 5G BERLIN association and since 2021 he is coordinator of the 6G-RIC (Research & Innovation Cluster).    

Teaching

    • Summer 2022

      • 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)
      • Master Project Network Information Systems (Dr.- Ing. Julius Schulz- Zander)

    • Winter 2021/22

      • VL Fundamentals of Digital Wireless Communication (Prof. Dr.-Ing. Slawomir Stanczak)
      • VL Mathematical Introduction to Machine Learning (Dr. rer. nat. Igor Bjelakovic)

    • Summer 2021

      • 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)
      • Master Project Network Information Systems (Dr.- Ing. Julius Schulz- Zander)

    • 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 [2]

    arXiv [3]

    LinkedIn [4]


    Publications [5]

    Preprints

    Fundamental properties of solutions to utility maximization problems in wireless networks
    Zitatschlüssel fundproperties_renato2017
    Autor R.L.G. Cavalcante and S. Stanczak
    Jahr 2018
    Journal arXiv:1610.01988
    Zusammenfassung We introduce a unified framework for the study of the utility and the energy efficiency of solutions to a large class of weighted max-min utility maximization problems in interference-coupled wireless networks. In more detail, given a network utility maximization problem parameterized by a maximum power budget p¯ available to network elements, we define two functions that map the power budget p¯ to the energy efficiency and to the utility achieved by the solution. Among many interesting properties, we prove that these functions are continuous and monotonic. In addition, we derive bounds revealing that the solutions to utility maximization problems are characterized by a low and a high power regime. In the low power regime, the energy efficiency of the solution can decrease slowly as the power budget increases, and the network utility grows linearly at best. In contrast, in the high power regime, the energy efficiency typically scales as Θ(1/p¯) as p¯→∞, and the network utility scales as Θ(1). We apply the theoretical findings to a novel weighted rate maximization problem involving the joint optimization of the uplink power and the base station assignment.
    Download Bibtex Eintrag [6]

    Zurück [7]

    Books

    S. Stanczak and M. Wiczanowski and H. Boche (2009). Fundamentals of Resource Allocation in Wireless Networks [8]. volume 3 of Foundations in Signal Processing, Communications and Networking. Springer, Berlin, 2009. Springer, Berlin.

    S. Stanczak and M. Wiczanowski and H. Boche (2006). Resource Allocation in Wireless Networks - Theory and Algorithms [9]. Lecture Notes in Computer Science (LNCS 4000). Springer, Berlin, 2006. Springer, Berlin.

    Book Chapters

    S. Stanczak and A. Keller and R.L.G. Cavalcante and N. Binder (2021). Long-term Perspectives: Machine Learning for Future Wireless Networks [10]. Chapter 14 in: Shaping Future 6G Networks: Needs, Impacts, and Technologies. John Wiley & Sons and IEEE Press.

    D. A. Awan and R.L.G. Cavalcante and M. Yukawa and S. Stanczak (2020). Adaptive Learning for Symbol Detection [11]. Machine Learning for Future Wireless Communications. Wiley & IEEE Press, 15.

    S. Maghsudi and S. Stanczak (2015). Communications in Interference-Limited Networks [12]. chapter in Distributed Channel Selection for Underlay Device-to-Device Communications: A Game- Theoretical Learning Framework. Springer International Publishing, 2015. Springer International Publishing.

    M. Goldenbaum and S. Stanczak and H. Boche (2015). Communications in Interference-Limited Networks [13]. chapter in Interference-Aware Analog Computation over the Wireless Channel: Fundamentals and Strategies. Springer International Publishing, 2015. Springer International Publishing.

    R. L. G. Cavalcante and S. Stanczak and I. Yamada (2014). Cooperative Cognitive Radios with Diffusion Networks [14]. 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 [15]. Smart Antennas–State-of-the-Art. Hindawi Publishing Corporation, 521-543.

    Journal Publications

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    M. Frey, I. Bjelakovic and S. Stanczak (2021). Over-The-Air Computation in Correlated Channels [23]. IEEE Transactions on Signal Processing

    F. Molinari, N. Agrawal, S. Stanczak and J. Raisch (2021). Max-Consensus Over Fading Wireless Channels [24]. IEEE Transactions on Control of Network Systems, Jan. 2021

    M. A. Gutierrez-Estevez, M. Kasparick and S. Stanczak (2021). Online Learning of Any-to-Any Path Loss Maps [25]. 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 [26]. IEEE Signal Processing Letters

    Fink, Jochen and Cavalcante, Renato Luís Garrido and Stanczak, Slawomir (2021). Multi-Group Multicast Beamforming by Superiorized Projections Onto Convex Sets [27]. IEEE Transactions on Signal Processing, 5708-5722.

    Fink, Jochen and Cavalcante, Renato Luís Garrido and Stanczak, Slawomir (2021). Multi-Group Multicast Beamforming by Superiorized Projections Onto Convex Sets [28]. IEEE Transactions on Signal Processing, 5708-5722.

    Miretti, Lorenzo and Cavalcante, Renato Luis Garrido and Stanczak, Slawomir (2021). Channel Covariance Conversion and Modelling Using Infinite Dimensional Hilbert Spaces [29]. IEEE Transactions on Signal Processing. IEEE, 3145–3159.

    Vural, Metin and Aravkin, Aleksandr Y. and Stanczak, Slawomir (2021). l1-Norm Minimization With Regula Falsi Type Root Finding Methods [30]. IEEE Signal Processing Letters, 2132-2136.

    D. A. Awan and R. L.G. Cavalcante and S. Stanczak (2020). Robust Cell-Load Learning with a Small Sample Set [31]. IEEE Transactions on Signal Processing (TSP), 68:270-283.

    C.-X. Wang and M. Di Renzo and S. Stanczak and S. Wang and E. G. Larsson (2020). Artificial Intelligence Enabled Wireless Networking for 5G and Beyond: Recent Advances and Future Challenges [32]. IEEE Wireless Communications Magazine, February 2020/Intelligent Radio: When Artificial Intelligence Meets Radio Network

    Sie sind auf Seite:1 | 2 [33] | 3 [34] | 4 [35] | 5 [36] | 6 [37] | 7 [38]

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    Conference, Symposium, and Workshop Papers

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    Attar, M. Hossein and Taghizadeh, Omid and Chang, Kaxin and Askar, Ramez and Mehlhose, Matthias and Stanczak, Slawomir (2022). Parallel APSM for Fast and Adaptive Digital SIC in Full-Duplex Transceivers with Nonlinearity [64]. 2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

    D.F. Külzer, F. Debbichi, S. Stanczak and M. Botsov (2021). On Latency Prediction with Deep Learning and Passive Probing at High Mobility [65]. IEEE International Conference on Communications (ICC) 2021, Montreal, Canada (virtual conference), in June 14-23, 2021

    M. Frey, I. Bjelakovic and S. Stanczak (2021). Over-The-Air Computation in Correlated Channels [66]. IEEE 2020 Information Theory Workshop (ITW), April 11-15, 2021

    D. Schäufele, M. Kasparick, J. Schwardmann, J. Morgenroth and S. Stanczak (2021). Terminal-Side Data Rate Prediction For High-Mobility Users [67]. IEEE 93rd Vehicular Technology Conference (VTC2021-Spring), Helsinki, Finland, April 2021

    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 [68]. 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 [69]. IEEE International Conference on Communications, 14-23 June 2021, Montreal, Canada

    Ismayilov, Rafail and Cavalcante, Renato LG and Stanczak, Slawomir (2021). Deep Learning Based Hybrid Precoding in Dual-Band Communication Systems [70]. ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 4115–4119.

    Dommel, Johannes and Wieruch, Dennis and Utkovski, Zoran and Stanczak, Slawomir (2021). A Semantics-Aware Communication Scheme to Estimate the Empirical Measure of A Quantity of Interest Via Multiple Access Fading Channels [71]. 2021 IEEE Statistical Signal Processing Workshop (SSP), 521–525.

    Ismayilov, Rafail and Cavalcante, Renato LG and Stanczak, Slawomir (2021). Deep Learning Beam Optimization in Millimeter-Wave Communication Systems [72]. 2021 IEEE Statistical Signal Processing Workshop (SSP), 581–585.

    Frey, Matthias and Bjelakovic, Igor and Stanczak, Slawomir (2021). Towards secure over-the-air computation [73]. 2021 IEEE International Symposium on Information Theory (ISIT), 700–705.

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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
    E-Mail-Anfrage [99]
    Webseite [100]

    Chairman of ITU-T Focus Group on Machine Learning for Future Networks including 5G

    Lupe [101]
    © ITU-T 2017- 2020
    Lupe [102]
    © TUBS GmbH
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