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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 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)
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Journal Publications
Citation key | WCM2020AI |
---|---|
Author | C.-X. Wang and M. Di Renzo and S. Stanczak and S. Wang and E. G. Larsson |
Year | 2020 |
Journal | IEEE Wireless Communications Magazine, February 2020/Intelligent Radio: When Artificial Intelligence Meets Radio Network |
Abstract | The fifth generation (5G) wireless communication networks are currently being deployed, and beyond 5G (B5G) networks are expected to be developed over the next decade. Artificial intelligence (AI) technologies and, in particular, machine learning (ML) have the potential to efficiently solve the unstructured and seemingly intractable problems by involving large amounts of data that need to be dealt with in B5G. This article studies how AI and ML can be leveraged for the design and operation of B5G networks. We first provide a comprehensive survey of recent advances and future challenges that result from bringing AI/ML technologies into B5G wireless networks. Our survey touches different aspects of wireless network design and optimization, including channel measurements, modeling, and estimation, physical-layer research, and network management and optimization. Then, ML algorithms and applications to B5G networks are reviewed, followed by an overview of standard developments of applying AI/ML algorithms to B5G networks. We conclude this study by the future challenges on applying AI/ML to B5G networks. |
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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
Contact
Prof. Dr.-Ing. Slawomir Stanczak