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TU Berlin

Inhalt des Dokuments

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Preprints

M. Frey, I. Bjelakovic and S. Stanczak (2021). Over-The-Air Computation in Correlated Channels. 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. Submitted to IEEE Transactions on Information Forensics and Security. Preprint available at arXiv:2001.03174


Books

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.


R. Freund, T. Haustein, M. Kasparick, K. Mahler, J. Schulz-Zander, L. Thiele, T. Wiegand, and R. Weiler (2018). 5G-Datentransport mit Höchstgeschwindigkeit. book chapter in R. Neugebauer (Ed.), "Digitalisierung: Schlüsseltechnologien für Wirtschaft und Gesellschaft" (pp. 89–111). Berlin, Heidelberg (2018)


G. Wunder, M. Kasparick, P. Jung, T. Wild, F. Schaich, Y. Chen, G. Fettweis, I. Gaspar, N. Michailow, M. Matthé, L. Mendes, D. Kténas, J.‐B. Doré, V. Berg, N. Cassiau, S. Pietrzyk, and M. Buczkowski (2016). New Physical‐layer Waveforms for 5G. book chapter in "Towards 5G: Applications, Requirements and Candidate Technologies'', Wiley, 2016, Eds. Rath Vannithamby and Shilpa Telwar


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.


I. Bjelakovic, H. Boche and J. Sommerfeld (2013). Capacity Results for Arbitrarily Varying Wiretap Channels. In: Aydinian H., Cicalese F., Deppe C. (eds) Information Theory, Combinatorics, and Search Theory. Lecture Notes in Computer Science, vol 7777. Springer, Berlin, Heidelberg


I. Bjelakovic, H. Boche, G. Janen and J. Notzel (2013). Arbitrarily Varying and Compound Classical-Quantum Channels and a Note on Quantum Zero-Error Capacities. In: Aydinian H., Cicalese F., Deppe C. (eds) Information Theory, Combinatorics, and Search Theory. Lecture Notes in Computer Science, vol. 7777. Springer, Berlin, Heidelberg


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


A. Pfadler, C. Ballesteros, J. Romeu and L. Jofre (2020). Hybrid Massive MIMO for Urban V2I: Sub-6 GHz vs mmWave Performance Assessment. IEEE Transactions on Vehicular Technology, 27 May 2020, pp. 4652-4662.


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


V. Stojkoski, Z. Utkovski, L. Basnarkov and L. Kocarev (2019). Cooperation dynamics in the networked geometric Brownian motion. Physical Review E 99, 062312, 28 June 2019


Conference, Symposium, and Workshop Papers

A hybrid model-data driven approach for the estimation of the angular power spectrum in massive MIMO systems
Zitatschlüssel Ren2020SSP
Autor R. L. G. Cavalcante and S. Stanczak
Jahr 2020
Journal IEEE Statistical Signal Processing Workshop 2020, Rio de Janeiro, Brazil, 12th-15th July, 2020 (to appear)
Zusammenfassung 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.
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