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TU Berlin

Inhalt des Dokuments

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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.


Cioni, Stefano and Lin, Xingqin and Chamaillard, Baptiste and El Jaafari, Mohamed and Charbit, Gilles and Raschkowski, Leszek (2022). Physical layer enhancements in 5G-NR for direct access via satellite systems. International Journal of Satellite Communications and Networking


Yunyan Chang and, Peter Jung and Chan Zhou and Sławomir Stańczak (2022). Distributed ranking-based resource allocation for sporadic M2M communication. EURASIP Journal on Wireless Communications and Networking



Mbugua, Allan Wainaina and Chen, Yun and Raschkowski, Leszek and Ji, Yilin and Gharba, Mohamed and Fan, Wei (2022). Efficient Pre-Processing of Site-Specific Radio Channels for Virtual Drive Testing in Hardware Emulators. IEEE Transactions on Aerospace and Electronic Systems, 1–14.


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


Patrick Agostini and Zoran Utkovski and Alexis Decurninge and Maxime Guillaud and Slawomir Stanczak (2022). Constant Weight Codes with Gabor Dictionaries and Bayesian Decoding for Massive Random Access. IEEE Transactions on Wireless Communications


Molinari, Fabio and Agrawal, Navneet and Stańczak, Sławomir and Raisch, Jörg (2022). Over-The-Air Max-Consensus in Clustered Networks Adopting Half-Duplex Communication Technology. IEEE Transactions on Control of Network Systems, 1-10.


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


Conference, Symposium, and Workshop Papers

Distributed Approximation of Functions over Fast Fading Channels with Applications to Distributed Learning and the Max-Consensus Problem
Zitatschlüssel BjeAller2019
Autor I. Bjelakovic and M. Frey and S. Stanczak
Jahr 2019
Journal 57th Annual Allerton Conference on Communication, Control, and Computing, 24-27 Sept. 2019 in Urbana, IL, USA, arXiv:1907.03777
Monat Sept.
Herausgeber IEEE
Zusammenfassung In this work, we consider the problem of distributed approximation of functions over multiple-access channels with additive noise. In contrast to previous works, we take fast fading into account and give explicit probability bounds for the approximation error allowing us to derive bounds on the number of channel uses that are needed to approximate a function up to a given approximation accuracy. Neither the fading nor the noise process is limited to Gaussian distributions. Instead, we consider sub-gaussian random variables which include Gaussian as well as many other distributions of practical relevance. The results are motivated by and have immediate applications to a) computing predictors in models for distributed machine learning and b) the max-consensus problem in ultra-dense networks.
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