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Prof. Dr.-Ing. Slawomir Stanczak

Lupe
© 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. 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 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)

  •  Winter 2018/19

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

  •  Summer 2018

    • VL Mathematical Methods in Signal Processing and Communications (Prof. Dr.-Ing. S.Stanczak)
    • VL Modern Signal Processing for Communications (Dr. R. Cavalcante)

  • Winter 2017/18

    • VL Fundamentals of Digital Wireless Communication
    • VL Selected topics in Wireless Network Optimization

  •  Summer 2017

    • VL Mathematical Methods in Signal Processing and Communications
    • VL Modern Signal Processing for Communications

  • Winter 2016/17

    • VL Fundamentals of Digital Wireless Communication
    • VL Selected topics in Wireless Network Optimization

  • Summer 2016

    • VL Wireless Network Analysis and Optimization
    • VL Modern Signal Processing for Communications

  • Winter 2015/16

    • VL Mathematical Methods in Signal Processing and Communications
    • SE Selected Topics in Wireless Network Optimization

  • Summer 2015

    • VL Cross-Layer Optimization and Resource Allocation in Wireless Networks

  • Winter 2014/15

    • VL Foundations of Digital and Wireless Communication

 

You can also find me on:

Fraunhofer Heinrich-Hertz-Institut

Google Scholar

arXiv

LinkedIn


Publications

Books

S. Stanczak and 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 and 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 and 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 and 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, pages 262{303. 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

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

Cheng-Xiang Wang, Marco Di Renzo, Slawomir Stanczak, Sen Wang, Erik G. Larsson (2020). Artificial Intelligence Enabled Wireless Networking for 5G and Beyond: Recent Advances and Future Challenges. IEEE Wireless Communications Magazine, February 2020/Intelligent Radio: When Artificial Intelligence Meets Radio Network

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 (accepted)

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

J. Schreck and P. Jung and S. Stanczak (2018). Compressive Rate Estimation with Applications to Device-to-Device Communications. IEEE Transactions on Wireless Communications

W. Samek and S. Stanczak and T. Wiegand (2017). The Convergence of Machine Learning and Communications. ITU Journal: ICT Discoveries, Special Issue No. 1

R.L.G. Cavalcante and M. Kasparick and S. Stanczak (2017). Max-Min Utility Optimization in Load Coupled Interference Networks. IEEE Transactions on Wireless Communications, vol. 16, no. 2, pp. 705-716, Feb. 2017

J. Mohammadi and S. Limmer and S. Stanczak (2016). A Decentralized Eigenvalue Computation Method for Spectrum Sensing Based on Average Consensus. Frequenz, July 2016

R.L.G. Cavalcante and S. Stanczak and J. Zhang and H. Zhuang (2016). Low complexity iterative algorithms for power estimation in ultra-dense load coupled networks. IEEE Trans. Signal Processing, vol. 64, no. 22, pp. 6058-6070, Nov. 2016

R.L.G. Cavalcante and Y. Shen and S. Stanczak (2016). Elementary Properties of Positive Concave Mappings With Applications to Network Planning and Optimization. IEEE Trans. Signal Processing, vol. 64, no. 7, pp. 1774-1783, April 2016

E. Pollakis and R.L.G. Cavalcante and S. Stanczak (2016). Traffic Demand-Aware Topology Control for Enhanced Energy-Efficiency of Cellular Networks. EURASIP Journal on Wireless Communications and Networks, vol. 2016, no. 1, pp. 1-17, Feb. 2016

G. Cao and P. Jung and S. Stanczak and F. Yu (2016). Data Aggregation and Recovery in Wireless Sensor Networks Using Compressed Sensing. International Journal of Sensor Networks (IJSNet), 209-219.

M. Kasparick and R. L. G. Cavalcante and S. Valentin and S. Stanczak and M. Yukawa (2015). Kernel-Based Adaptive Online Reconstruction of Coverage Maps With Side Information. IEEE Transactions on Vehicular Technology, vol. 65, no. 7, pp. 5461-5473, July 2016 (also available at http://arxiv.org/abs/1404.0979)

S. Maghsudi and S. Stanczak (2015). Hybrid Centralized-Distributed Resource Allocation for Device-to-Device Communication Underlaying Cellular Networks. IEEE Trans. Veh. Technol., 2481-2495.

M. Goldenbaum and H. Boche and S. Stanczak (2015). Nomographic Functions: Efficient Computation in Clustered Gaussian Sensor Networks. IEEE Trans. Wireless Commun., 2093–2105.

S. Maghsudi and S. Stanczak (2015). Channel Selection for Network-Assisted D2D Communication via No-Regret Bandit Learning With Calibrated Forecasting. IEEE Transactions on Wireless Communications, 1309-1322.

F. Penna and S. Stanczak (2015). Decentralized Eigenvalue Algorithms for Distributed Signal Detection in Cognitive Networks. IEEE Transactions on Signal Processing, 63(2):427-440, Jan. 2015

R. Cavalcante and S. Stanczak and M. Schubert and A. Eisenblätter and U. Türke (2014). Toward Energy-Efficient 5G Wireless Communications Technologies. IEEE Signal Processing Magazine, vol. 31, no. 6, pp. 24-34, Nov. 2014

M. Goldenbaum and S. Stanczak (2014). On the Channel Estimation Effort for Analog Computation Over Wireless Multiple-Access Channels. IEEE Wireless Communications Letters, 261-264.

G. Cao and P. Jung and S. Stanczak (2014). Low Cost Error Correction for Multi-hop Data Aggregation Using Compressed Sensing. IEICE Trans. on Information and System

A. Giovanidis and Q. Liao and S. Stanczak (2014). Measurement-Adaptive Cellular Random Access Protocols. Wireless Networks. Springer Verlag (Germany), 1495-1514.

M. Goldenbaum and H. Boche and S. Stanczak (2013). Harnessing Interference for Analog Function Computation in Wireless Sensor Networks. IEEE Transactions on Signal Processing, 4893–4906.

M. Goldenbaum and S. Stanczak (2013). Robust Analog Function Computation via Wireless Sensor Multiple-Access Channels. IEEE Transactions on Communications, 3863-3877.

M. Zheng and P. Pawelczak and S. Stanczak and H. Yu (2013). Planning of Cellular Networks Enhanced by Energy Harvesting. IEEE Communications Letters, 1092–1095.

R.L.G. Cavalcante and S. Stanczak (2013). A Distributed Subgradient Method for Dynamic Convex Optimization Problems under Noisy Information Exchange. IEEE Journal of Selected Topics in Signal Processing, vol. 7, no. 2, pp. 243-256, April 2013

I. Koutsopoulos and S. Stanczak (2012). The Impact of Transmit Rate Control on Energy-efficient Estimation in Wireless Sensor Networks. IEEE Trans. Wireless Commun., 3261–3271.

A. Giovanidis and S. Stanczak (2011). Stability and Distributed Power Control in MANETs with Per Hop Retransmissions. IEEE Transactions on Communications, 1632 -1643.

S. Stanczak and M. Kaliszan and N. Bambos (2010). A Characterization of Max-Min SIR-Balanced Power Allocation with Applications. Wireless Networks (Springer), 2335–2347.

S. Stanczak and A. Feistel and M. Wiczanowski and H. Boche (2010). Utility-based Power Control with QoS Support. Wireless Networks (Springer), 1691–1705.

A. Feistel and S. Stanczak and D. Tomecki (2010). Joint Utility-Based Power Control and Receive Beamforming in Decentralized Wireless Networks. EURASIP Journal on Wireless Communications and Networking.

S. Stanczak and M. Kaliszan and N. Bambos (2010). Decentralized Admission Control for Power-Controlled Wireless Links. CoRR

M. Wiczanowski and H. Boche and S. Stanczak (2009). An Algorithm for Optimal Resource Allocation in Cellular Networks with Elastic Traffic. IEEE Transactions on Communications, 41–44.

H. Boche and S. Stanczak (2008). Strict Convexity of the Log-SIR region. IEEE Transactions on Communications, 1511–1518.

M. Wiczanowski and S. Stanczak and H. Boche (2008). Performance and Interference Control in Wireless Ad hoc and Mesh Networks— A Generalized Lagrangian Approach. IEEE Transactions on Signal Processing, 4039–4052.

M. Wiczanowski and S. Stanczak and H. Boche (2008). Providing Quadratic Convergence of Decentralized Power Control in Wireless Networks—The Method of Min-Max Functions. IEEE Transactions on Signal Processing, 4053–4068.

H. Boche and M. Wiczanowski and S. Stanczak (2008). On Optimal Resource Allocation in Cellular Networks with Best-Effort Traffic. IEEE Transactions on Wireless Communications, 1163–1167.

S. Stanczak and M. Wiczanowski and H. Boche (2007). Distributed Utility-Based Power Control: Objectives and Algorithms. IEEE Trans. Signal Processing, 5058–5068.

H. Boche and S. Stanczak (2007). A Note on Some Properties of the Perron Root of Nonnegative Irreducible Matrices. Appl. Alg. Eng. Comm. Comp., 369–378.

S. Stanczak and H. Boche (2007). On the Convexity of Feasible QoS Regions. IEEE Transactions on Information Theory, 779-783.

H. Boche and M. Wiczanowski and S. Stanczak (2007). Unifying View on Min-Max Fairness, Max-Min Fairness, and Utility Optimization in Cellular Networks. EURASIP J. Wireless Commun. and Net.

H. Boche and S. Stanczak (2006). The Kullback–Leibler Divergence and Nonnegative Matrices. IEEE Transactions on Information Theory, 5539-5545.

S. Stanczak and H. Boche (2006). The Infeasible SIR Region Is Not a Convex Set. IEEE Transactions on Communications, 1905-1907.

S. Stanczak and G. Wunder and H. Boche (2006). On Pilot-based Multipath Channel Estimation for Uplink CDMA Systems: An Overloaded Case. IEEE Trans. Signal Processing, 512-519.

H. Boche and S. Stanczak (2005). Log-Convexity of the Minimum Total Power in CDMA Systems with Certain Quality-Of-Service Guaranteed. IEEE Transactions on Information Theory, 374-381.

H. Boche and S. Stanczak (2004). Convexity of Some Feasible QoS Regions and Asymptotic Behavior of the Minimum Total Power in CDMA Systems. IEEE Transactions on Communications, 2190-2197.

H. Boche and S. Stanczak (2004). On Systems of Linear Equations with Nonnegative Coefficients. Appl. Alg. Eng. Comm. Comp., 397-414.

H. Boche and S. Stanczak (2003). Optimal allocation of resources in an asynchronous CDMA channel with identical SINR requirements for all users. IEICE Trans. on Communications, 397-405.

Conference, Symposium, and Workshop Papers

Machine Learning-Based Adaptive Receive Filtering: Proof-of-Concept on an SDR Platform
Citation key Mehl2020ICC
Author M. Mehlhose, D. A. Awan, R. L.G. Cavalcante, M. Kurras and S. Stanczak
Year 2020
Journal accepted, IEEE International Conference on Communications (ICC), Dublin, Ireland, 2020
Abstract Conventional multiuser detection techniques either require a large number of antennas at the receiver for a desired performance, or they are too complex for practical implementation. Moreover, many of these techniques, such as successive interference cancellation (SIC), suffer from errors in parameter estimation (user channels, covariance matrix, noise variance, etc.) that is performed before detection of user data symbols. As an alternative to conventional methods, this paper proposes and demonstrates a low-complexity practical Machine Learning (ML) based receiver that achieves similar (and at times better) performance to the SIC receiver. The proposed receiver does not require parameter estimation; instead it uses supervised learning to detect the user modulation symbols directly. We perform comparisons with minimum mean square error (MMSE) and SIC receivers in terms of symbol error rate (SER) and complexity.
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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

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