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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

Machine Learning-Based Adaptive Receive Filtering: Proof-of-Concept on an SDR Platform
Citation key Mehl2020ICC
Author M. Mehlhose and D. A. Awan and R. L.G. Cavalcante and M. Kurras and S. Stanczak
Year 2020
Journal accepted, IEEE International Conference on Communications (ICC), Dublin, Ireland, 2020
Abstract Conventional multiuser detection techniques either require a large number of antennas at the receiver for a desired performance, or they are too complex for practical implementation. Moreover, many of these techniques, such as successive interference cancellation (SIC), suffer from errors in parameter estimation (user channels, covariance matrix, noise variance, etc.) that is performed before detection of user data symbols. As an alternative to conventional methods, this paper proposes and demonstrates a low-complexity practical Machine Learning (ML) based receiver that achieves similar (and at times better) performance to the SIC receiver. The proposed receiver does not require parameter estimation; instead it uses supervised learning to detect the user modulation symbols directly. We perform comparisons with minimum mean square error (MMSE) and SIC receivers in terms of symbol error rate (SER) and complexity.
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