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Preprints

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


C. Bockelmann and others (2018). Towards Massive Connectivity Support for Scalable mMTC Communications in 5G networks. Preprint (available at https://arxiv.org/abs/1804.01701)


R.L.G. Cavalcante and S. Stanczak (2018). Spectral radii of asymptotic mappings and the convergence speed of the standard fixed point algorithm. Preprint (available at https://arxiv.org/abs/1803.05671v1)


J. Fink and R. L.G. Cavalcante and P. Jung and S. Stanczak (2018). Extrapolated Projection methods for PAPR Reduction. Preprint, accepted for publication, 26th European Signal Processing Conference (EUSIPCO 2018)


D. Schaeufele and R. L.G. Cavalcante and Z. Zhong and S. Stanczak (2018). Tensor Completion for Radio Map Reconstruction and Channel Cartography using Low Rank and Smoothness. Preprint


M. Raceala-Motoc and P. Jung and Z. Utkovski and S. Stanczak (2018). C-RAN-Assisted Non-Coherent Grant-Free Random Access Based on Compute-and-Forward.


R.L.G. Cavalcante and S. Stanczak (2018). Fundamental properties of solutions to utility maximization problems in wireless networks. arXiv:1610.01988


Y. Chang and P. Jung and C. Zhou and S. Stanczak (2016). Block Compressed Sensing Based Distributed Device Detection for M2M Communications. Preprint (available at https://arxiv.org/abs/1609.05080v1)


Books

S. Stanczak and 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 and 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

S. Stanczak and A. Keller and R.L.G. Cavalcante and N. Binder (2021). Long-term Perspectives: Machine Learning for Future Wireless Networks. Chapter 14 in: Shaping Future 6G Networks: Needs, Impacts, and Technologies. John Wiley & Sons and IEEE Press.


D. A. Awan and R.L.G. Cavalcante and 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 in Distributed Channel Selection for Underlay Device-to-Device Communications: A Game- Theoretical Learning Framework. Springer International Publishing, 2015. Springer International Publishing.


M. Goldenbaum and S. Stanczak and H. Boche (2015). Communications in Interference-Limited Networks. chapter in Interference-Aware Analog Computation over the Wireless Channel: Fundamentals and Strategies. Springer International Publishing, 2015. Springer International Publishing.


R. L. G. Cavalcante and 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 and 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 and H. Boche and 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

Hernangómez, Rodrigo and Visentin, Tristan and Servadei, Lorenzo and Khodabakhshandeh, Hamid and Sta'nczak, Sławomir (2022). Improving Radar Human Activity Classification Using Synthetic Data with Image Transformation. Sensors. Multidisciplinary Digital Publishing Institute, 1519.


K. Komuro and M. Yukawa and R. L. G. Cavalcante (2022). Distributed Sparse Optimization with Weakly Convex Regularizer: Consensus Promoting and Approximate Moreau Enhanced Penalties towards Global Optimality. Transactions on Signal and Information Processing over Networks


K. Komuro and M. Yukawa and R. L. G. Cavalcante (2022). Distributed Sparse Optimization with Weakly Convex Regularizer: Consensus Promoting and Approximate Moreau Enhanced Penalties towards Global Optimality. Transactions on Signal and Information Processing over Networks


Nicola Kleppmann and Johannes Dommel and Dennis Wieruch and Stefan Erben (2021). 5G and NOA: Enabling access to valuable hidden data. atp!info Magazin


M. Frey, I. Bjelakovic and S. Stanczak (2021). Over-The-Air Computation in Correlated Channels. IEEE Transactions on Signal Processing


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


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


Taghizadeh, Omid and Stanczak, Slawomir and Iimori, Hiroki and De Abreu, Giuseppe Thadeu Freitas (2021). Full-Duplex Amplify-and-Forward MIMO Relaying: Design and Performance Analysis Under Erroneous CSI and Hardware Impairments. IEEE Open Journal of the Communications Society. IEEE, 1249–1266.


Miretti, Lorenzo and Cavalcante, Renato L.G. and Stanczak, Slawomir (2021). Channel Covariance Conversion and Modelling Using Infinite Dimensional Hilbert Spaces. IEEE Transactions on Signal Processing. IEEE, 3145–3159.


Conference, Symposium, and Workshop Papers

Pulse-Shaped OTFS for V2X Short-Frame Communication with Tuned One-Tap Equalization
Citation key pfad2020itg
Author A. Pfadler and P. Jung and S. Stanczak
Year 2020
ISBN 978-3-8007-5200-3
Location Hamburg, Germany
Journal 24th International ITG Workshop on Smart Antennas, Poster presentation, February 18-20, 2020 in Hamburg, Germany
Month Feb.
Editor VDE
Abstract The recently proposed orthogonal time frequency and space (OTFS) waveform promises significant performance advantages for high mobility users. OTFS is a pulse-shaped multicarrier scheme with Gabor (Weyl-Heisenberg) structure and additional time-frequency (TF) spreading. The spreading of the symbols over the TF domain is utilizing the symplectic Fourier transform (SFFT). For coherent communication OTFS requires proper channel information and the use of appropriated equalizers to exploit the full spreading gain. The doubly-dispersive channel is first estimated with pilot and guard symbols placed in the delay-Doppler (DD) domain. One-tap equalization, favored due to their lower complexity, is then performed in the TF domain. However, an accurate estimation of the self-interference power is necessary to exploit full diversity with such linear equalizer. To enable short-frame vehicle-to-everything (V2X) communication, we propose to instantaneously estimate the selfinterference power using pilot and guard symbols for tuning the minimum mean square error (MMSE) equalizer. We evaluate this approach for vehicular channels generated by the geometricstatistical channel simulator Quadriga and with an OTFS transceiver architecture based on a polyphase implementation for orthogonalized Gaussian pulses. Comparing to a IEEE 802.11p compliant design, we show that in such vehicular scenarios OTFS significantly outperforms cyclic-prefix based orthogonal frequency-division multiplexing (OFDM) when instantaneously tuning the equalizer. Our results also indicate that with such an approach the promised OTFS gains can indeed be obtained with a linear equalizer.
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