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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


Books

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

Stańczak, Sławomir and Keller, Alexander and Cavalcante, Renato LG and Binder, Nikolaus (2021). Long-term Perspectives: Machine Learning for Future Wireless Networks. Shaping Future 6G Networks: Needs, Impacts, and Technologies. John Wiley & Sons.


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

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. 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


Stojkoski, Viktor and Karbevski, Marko and Utkovski, Zoran and Basnarkov, Lasko and Kocarev, Ljupco (2021). Evolution of cooperation in networked heterogeneous fluctuating environments. Physica A: Statistical Mechanics and its Applications. Elsevier, 125904.


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 Lu'\is Garrido and Sta'nczak, Slawomir (2021). Channel Covariance Conversion and Modelling Using Infinite Dimensional Hilbert Spaces. IEEE Transactions on Signal Processing. IEEE, 3145–3159.


Frey, Matthias and Bjelaković, Igor and Stańczak, Sławomir (2021). Over-the-Air Computation in Correlated Channels. IEEE Transactions on Signal Processing, 5739-5755.


Fink, Jochen and Cavalcante, Renato Luís Garrido and Stańczak, Sławomir (2021). Multi-Group Multicast Beamforming by Superiorized Projections Onto Convex Sets. IEEE Transactions on Signal Processing, 5708-5722.


Fink, Jochen and Cavalcante, Renato Luís Garrido and Stańczak, Sławomir (2021). Multi-Group Multicast Beamforming by Superiorized Projections Onto Convex Sets. IEEE Transactions on Signal Processing, 5708-5722.


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, 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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