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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


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

Mobility Modes for Pulse-Shaped OTFS with Linear Equalizer
Citation key pfad2020gc
Author A. Pfadler, P. Jung and S. Stanczak
Year 2020
Journal IEEE Globecom 2020, December 7-11, Taipei, Taiwan
Month Dec
Editor IEEE
Abstract Orthogonal time frequency and space (OTFS) modulation is a pulse-shaped Gabor signaling scheme with additional time-frequency (TF) spreading using the symplectic finite Fourier transform (SFFT). With sufficient accurate channel information and sophisticated equalizers it promises performance gains in terms of robustness for high mobility users. To fully exploit diversity in OTFS, the 2D-deconvolution implemented by a linear equalizer should approximately invert the doubly dispersive channel operation, which however is a twisted convolution. In theory, this is achieved in a first step by matching the TF grid and the Gabor synthesis and analysis pulses to the delay and Doppler spread of the channel. However, in practice, one always has to balance between supporting high granularity in delay-Doppler (DD) spread, and multi-user and network aspects. In this paper, we propose mobility modes with distinct grid and pulse matching for different doubly dispersive channel. To account for remaining self-interference, we tune the minimum mean square error (MMSE) linear equalizer without the need of estimating channel cross talk coefficients. We evaluate our approach with the QuaDRiGa channel simulator and with OTFS transceiver architecture based on a polyphase implementation for orthogonalized Gaussian pulses. In addition, we compare OTFS to a IEEE 802.11p compliant design of cyclic prefix (CP) based orthogonal frequency-division multiplexing (OFDM). Our results indicate that with an appropriate mobility mode, the potential OTFS gains can be indeed achieved with linear equalizers to significantly outperforms OFDM.
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