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Inhalt des Dokuments


M. Frey, I. Bjelakovic and S. Stanczak (2021). Over-The-Air Computation in Correlated Channels [1]. 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 [2]. Submitted to IEEE Transactions on Information Forensics and Security. Preprint available at arXiv:2001.03174


S. Stanczak, M. Wiczanowski and H. Boche (2009). Fundamentals of Resource Allocation in Wireless Networks [3]. 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 [4]. 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 [5]. 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 [6]. 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 [7]. 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 [8]. 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 [9]. 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 [10]. 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 [11]. 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 [12]. 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 [13]. 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 [23]. 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 [24]. IEEE Signal Processing Letters

F. Molinari, N. Agrawal, S. Stanczak and J. Raisch (2021). Max-Consensus Over Fading Wireless Channels [25]. 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 [26]. 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 [27]. 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 [28]. 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 [29]. 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 [30]. 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 [31]. 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 [32]. Physical Review E 99, 062312, 28 June 2019

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Conference, Symposium, and Workshop Papers

Predictive Quality of Service: Adaptation of Platoon Inter-Vehicle Distance to Packet Inter-Reception Time
Citation key pfadVtc2020
Author A. Pfadler, G. Jomod, A. E. Assaad and P. Jung
Pages pp. 1-5
Year 2020
ISBN 978-1-7281-5207-3
ISSN 2577-2465
DOI 10.1109/VTC2020-Spring48590.2020.9129097
Location Antwerp, Belgium, Belgium
Journal IEEE 91st Vehicular Technology Conference (VTC2020-Spring), Antwerp, Belgium, 2020
Month May
Editor IEEE
Abstract Vehicle-to-everything (V2X) communication is seen as an enabler of high-density platooning as part of more environmentally friendly future transportation systems. Indeed, in high-density platooning, trucks are able to reduce their overall fuel consumption. Compared to platooning systems exclusively based on sensors, V2X enabled platooning systems can drive smaller inter-vehicle distances. They are then able to achieve this fuel consumption reduction thanks to the decreased air drag. It has been shown that the performance of the application is dependent on the performance of the communications system. The application therefore needs to be aware of the maximal tolerable communication degradation that keeps the platoon safe considering its driving parameters. In this article, we derive the relationship between the maximal tolerable packet losses, measured as the packet inter-reception time, and the intervehicle distance. We first study the relationship between these parameters through the analysis of simulation data. We then derive a functional link by fitting different statistical models. Finally, we apply the resulting models to packet inter-reception time measurements obtained in simulation of platoons supported by IEEE 802. 11p driving through varying surrounding traffic densities.
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