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Inhalt des Dokuments

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T. Piotrowski and R. L. G. Cavalcante (2021). Fixed points of monotonic and (weakly) scalable neural networks. arXiv preprint arXiv:2106.16239

T. Piotrowski and R. L. G. Cavalcante (2021). The fixed point iteration of positive concave mappings converges geometrically if a fixed point exists. arXiv preprint arXiv:2110.11055

M. Frey, I. Bjelakovic and S. Stanczak (2020). Towards Secure Over-The-Air Computation. Submitted to Problems of Information Transmission. Preprint available at arXiv:2001.03174


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

Stańczak, Sławomir and Keller, Alexander and Cavalcante, Renato LG and Binder, Nikolaus (2021). Long-term Perspectives: Machine Learning for Future Wireless Networks. Shaping Future 6G Networks: Needs, Impacts, and Technologies. John Wiley & Sons.

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

Robust Cell-Load Learning with a Small Sample Set
Zitatschlüssel Robawan2019
Autor D. A. Awan, R. L.G. Cavalcante and S. Stanczak
Seiten 68:270-283
Jahr 2020
DOI 10.1109/TSP.2019.2959221
Journal IEEE Transactions on Signal Processing (TSP)
Jahrgang 68
Nummer 68
Monat Jan.
Zusammenfassung Learning of the cell-load in radio access networks (RANs) has to be performed within a short time period. Therefore, we propose a learning framework that is robust against uncertainties resulting from the need for learning based on a relatively small training set. To this end, we incorporate prior knowledge about the cell-load in the learning framework. For example, an inherent property of the cell-load is that it is monotonic in downlink (data) rates. To obtain additional prior knowledge we first study the feasible rate region, i.e., the set of all vectors of user rates that can be supported by the network. We prove that the feasible rate region is compact. Moreover, we show the existence of a Lipschitz function that maps feasible rate vectors to cell-load vectors. With these results in hand, we present a learning technique that guarantees a minimum approximation error in the worst-case scenario by using prior knowledge and a small training sample set. Simulations in the network simulator NS3 demonstrate that the proposed method exhibits better robustness and accuracy than standard learning techniques, especially for small training sample sets.
Download Bibtex Eintrag

Conference, Symposium, and Workshop Papers

K. Komuro and M. Yukawa and R. L. G. Cavalcante (2021). Distributed Sparse Optimization: Towards Global Optimality using Weakly Convex Regularizers. Proc. IEICE Signal Processing Symposium

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

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

M. Frey, I. Bjelakovic and S. Stanczak (2021). Over-The-Air Computation in Correlated Channels. Accepted for publication at IEEE 2020 Information Theory Workshop (ITW), April 11-15, 2021, final version on arXiv:2101.04690

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. 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. IEEE International Conference on Communications, 14-23 June 2021, Montreal, Canada

Undi, Fabian and Schultze, Alper and Keusgen, Wilhelm and Peter, Michael and Eichler, Taro (2021). Angle-Resolved THz Channel Measurements at 300 GHz in an Outdoor Environment. 2021 IEEE International Conference on Communications Workshops (ICC Workshops), 1–7.

Schultze, Alper and Wittig, Sven and Keusgen, Wilhelm (2021). Spatially Resolved Multi-Transmitter Ka-Band Channel Measurements for Receiver Localization. 2021 IEEE Radio and Wireless Symposium (RWS), 154–157.

Gutierrez-Estevez, Miguel A and Utkovski, Zoran and Kousaridas, Apostolos and Zhou, Chan (2021). A Statistical Learning Framework for QoS Prediction in V2X. 2021 IEEE 4th 5G World Forum (5GWF), 441–446.

Bezmenov, Maria and Utkovski, Zoran and Sambale, Klaus and Stanczak, Slawomir (2021). Semi-Persistent Scheduling with Single Shot Transmissions for Aperiodic Traffic. 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring), 1–7.

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