Inhalt des Dokuments

[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.
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 [2]
arXiv
[3]
LinkedIn [4]
Publications
[5]
Preprints
M. Frey, I. Bjelakovic and S. Stanczak (2021).
Over-The-Air Computation in Correlated Channels [6]. 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 [7]. Submitted to IEEE
Transactions on Information Forensics and Security.
arXiv:2001.03174
Books
S. Stanczak, 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, 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
D. A. Awan, R.L.G. Cavalcante, M. Yukawa and S.
Stanczak (2020). Adaptive Learning for Symbol Detection [10].
Machine Learning for Future Wireless Communications. Wiley
& IEEE Press, 15.
S. Maghsudi and S. Stanczak (2015).
Communications in Interference-Limited Networks [11]. 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 [12]. 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 [13].
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 [14]. Smart Antennas–State-of-the-Art. Hindawi
Publishing Corporation, 521-543.
Journal
Publications
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M. A. Gutierrez-Estevez, M. Kasparick and S.
Stanczak (2021). Online Learning of Any-to-Any Path Loss Maps [17].
IEEE Communications Letters (accepted)
D. A. Awan, Renato L.G. Cavalcante and Slawomir
Stanczak (2020). Robust Cell-Load Learning with a Small Sample Set
[18]. 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 [19]. 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
[20]. 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 [21]. ITU News Magazine - AI and Machine
Learning in 5G, no. 5, p. 55, open access, Dec.
F. Molinari, N. Agrawal, S. Stanczak and J.
Raisch (2020). Max-Consensus Over Fading Wireless Channels [22].
IEEE Transactions on Control of Network Systems, Dec. 2020
R. L.G. Cavalcante, Q. Liao and S. Stanczak
(2019). Connections between spectral properties of asymptotic mappings
and solutions to wireless network problems [23]. 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 [24]. 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 [25]. IEEE Access (Volume: 6),
pages 28969 - 28992, May 16, 2018
J. Schreck, P. Jung and S. Stanczak (2018).
Compressive Rate Estimation with Applications to Device-to-Device
Communications [26]. IEEE Transactions on Wireless
Communications
W. Samek, S. Stanczak and T. Wiegand (2017). The
Convergence of Machine Learning and Communications [27]. ITU
Journal: ICT Discoveries, Special Issue No. 1
R.L.G. Cavalcante, M. Kasparick and S. Stanczak
(2017). Max-Min Utility Optimization in Load Coupled Interference
Networks [28]. IEEE Transactions on Wireless Communications, vol.
16, no. 2, pp. 705-716, Feb. 2017
J. Mohammadi, S. Limmer and S. Stanczak (2016). A
Decentralized Eigenvalue Computation Method for Spectrum Sensing Based
on Average Consensus [29]. Frequenz, July 2016
R.L.G. Cavalcante, S. Stanczak, J. Zhang and H.
Zhuang (2016). Low complexity iterative algorithms for power
estimation in ultra-dense load coupled networks [30]. 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 [31]. IEEE
Trans. Signal Processing, vol. 64, no. 7, pp. 1774-1783, April
2016
E. Pollakis, R.L.G. Cavalcante and S. Stanczak
(2016). Traffic Demand-Aware Topology Control for Enhanced
Energy-Efficiency of Cellular Networks [32]. 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 [33]. International Journal of Sensor
Networks (IJSNet), 209-219.
S. Maghsudi and S. Stanczak (2015). Joint Channel
Selection and Power Control in Infrastructureless Wireless Networks: A
Multiplayer Multiarmed Bandit Framework [34]. IEEE Trans. Veh.
Technol., 4565-4578.
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 [35].
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 [36]. IEEE Trans.
Veh. Technol., 2481-2495.
M. Goldenbaum and H. Boche and S. Stanczak
(2015). Nomographic Functions: Efficient Computation in Clustered
Gaussian Sensor Networks [37]. 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 [38]. IEEE Transactions on
Wireless Communications, 1309-1322.
F. Penna and S. Stanczak (2015). Decentralized
Eigenvalue Algorithms for Distributed Signal Detection in Cognitive
Networks [39]. 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 [40]. 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 [41]. 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 [42]. IEICE Trans. on Information and System
A. Giovanidis and Q. Liao and S. Stanczak (2014).
Measurement-Adaptive Cellular Random Access Protocols [43].
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 [44]. IEEE Transactions on Signal
Processing, 4893–4906.
M. Goldenbaum and S. Stanczak (2013). Robust
Analog Function Computation via Wireless Sensor Multiple-Access
Channels [45]. 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
[46]. 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 [47]. 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 [48]. IEEE Trans. Wireless Commun.,
3261–3271.
A. Giovanidis and S. Stanczak (2011). Stability
and Distributed Power Control in MANETs with Per Hop Retransmissions
[49]. 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 [50]. Wireless Networks (Springer),
2335–2347.
S. Stanczak and A. Feistel and M. Wiczanowski and
H. Boche (2010). Utility-based Power Control with QoS Support [51].
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 [52]. EURASIP Journal on Wireless
Communications and Networking.
S. Stanczak and M. Kaliszan and N. Bambos (2010).
Decentralized Admission Control for Power-Controlled Wireless Links
[53]. CoRR
M. Wiczanowski and H. Boche and S. Stanczak
(2009). An Algorithm for Optimal Resource Allocation in Cellular
Networks with Elastic Traffic [54]. IEEE Transactions on
Communications, 41–44.
H. Boche and S. Stanczak (2008). Strict Convexity
of the Log-SIR region [55]. 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 [56]. 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 [57]. 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 [58]. IEEE Transactions on Wireless
Communications, 1163–1167.
S. Stanczak and M. Wiczanowski and H. Boche
(2007). Distributed Utility-Based Power Control: Objectives and
Algorithms [59]. 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
[60]. Appl. Alg. Eng. Comm. Comp., 369–378.
S. Stanczak and H. Boche (2007). On the Convexity
of Feasible QoS Regions [61]. 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 [62]. EURASIP J.
Wireless Commun. and Net.
H. Boche and S. Stanczak (2006). The
Kullback–Leibler Divergence and Nonnegative Matrices [63]. IEEE
Transactions on Information Theory, 5539-5545.
S. Stanczak and H. Boche (2006). The Infeasible
SIR Region Is Not a Convex Set [64]. 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 [65]. 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 [66]. IEEE Transactions on
Information Theory, 374-381.
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Conference, Symposium, and
Workshop Papers
A hybrid model-data driven approach for the
estimation of the angular power spectrum in massive MIMO
systems
Citation key |
Ren2020SSP |
Author |
R. L. G.
Cavalcante and S. Stanczak |
Year |
2020 |
Journal |
IEEE
Statistical Signal Processing Workshop 2020, Rio de Janeiro, Brazil,
12th-15th July, 2020 (to appear) |
Abstract |
Information about the angular power spectra (APS) of signals
impinging on antenna arrays have important applications in massive
MIMO systems such as user clustering, angle-of-arrival estimation, and
channel covariance estimation in FDD systems, to cite a few. Current
approaches for the estimation of APS can be divided into two main
groups: model based methods and data driven methods. The former
methods are able to produce reliable estimates with little side
information and no training , but they do not exploit any information
from datasets (if available) to improve the estimates. In contrast,
pure data-driven methods can provided good performance without any
knowledge about physical models, but they do not provide any
guarantees of robustness against sudden changes in the propagation
environment, a common occurrence in wireless systems. Against this
background, we propose novel hybrid model and data driven algorithms
that use both information about models and any available dataset. To
this end, we modify projection and optimization methods for APS
estimation by using an inner product (and hence the induced norm and
metric) that is learned from data. The proposed algorithms are able to
produce reliable estimates that exploit both statistical information
available in datasets and model knowledge. As a result, they are able
to provide robustness against relatively large changes in the
propagation environment without the need to perform frequent
training. |
Download Bibtex entry
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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 query [72]
Website [73]

[74]- © TUBS GmbH
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[71] https://matomo.org/
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[74]
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