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# Preprints

Citation key | fundproperties_renato2017 |
---|---|

Author | R.L.G. Cavalcante and S. Stanczak |

Year | 2018 |

Journal | arXiv:1610.01988 |

Abstract | We introduce a unified framework for the study of the utility and the energy efficiency of solutions to a large class of weighted max-min utility maximization problems in interference-coupled wireless networks. In more detail, given a network utility maximization problem parameterized by a maximum power budget p¯ available to network elements, we define two functions that map the power budget p¯ to the energy efficiency and to the utility achieved by the solution. Among many interesting properties, we prove that these functions are continuous and monotonic. In addition, we derive bounds revealing that the solutions to utility maximization problems are characterized by a low and a high power regime. In the low power regime, the energy efficiency of the solution can decrease slowly as the power budget increases, and the network utility grows linearly at best. In contrast, in the high power regime, the energy efficiency typically scales as Θ(1/p¯) as p¯→∞, and the network utility scales as Θ(1). We apply the theoretical findings to a novel weighted rate maximization problem involving the joint optimization of the uplink power and the base station assignment. |

# 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

Hernangómez, Rodrigo and Bjelakovic, Igor and Servadei, Lorenzo and Sta'nczak, Sławomir (2022). Unsupervised Domain Adaptation across FMCW Radar Configurations Using Margin Disparity Discrepancy. *2022 30th European Signal Processing Conference (EUSIPCO)*

Kei Komuro and Masahiro Yukawa and Renato L. G. Cavalcante (2022). Distributed Sparse Optimization Based on Minimax Concave and Consensus Promoting Penalties: Towards Global Optimality. *2022 30th European Signal Processing Conference (EUSIPCO)*

Fink, Jochen and Cavalcante, Renato L. G. and Utkovski, Zoran and Sta'nczak, Sławomir (2022). A Set-Theoretic Approach to MIMO Detection. *ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)*, 5328–5332.

Hernangómez, Rodrigo and Palaios, Alexandros and Guruvayoorappan, Gayathri and Kasparick, Martin and Ain, Noor Ul and Sta'nczak, Sławomir (2022). Online QoS Estimation for Vehicular Radio Environments. *2022 30th European Signal Processing Conference (EUSIPCO)*, 5.

Attar, M. Hossein and Taghizadeh, Omid and Chang, Kaxin and Askar, Ramez and Mehlhose, Matthias and Stanczak, Slawomir (2022). Parallel APSM for Fast and Adaptive Digital SIC in Full-Duplex Transceivers with Nonlinearity. *2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)*

K. Komuro and M. Yukawa and R. L. G. Cavalcante (2021). Distributed Sparse Optimization: Towards Global Optimality using Weakly Convex Regularizers. *Proc. IEICE Signal Processing Symposium*

D.F. Külzer, F. Debbichi, S. Stanczak and M. Botsov (2021). On Latency Prediction with Deep Learning and Passive Probing at High Mobility. *IEEE International Conference on Communications (ICC) 2021, Montreal, Canada (virtual conference), in June 14-23, 2021*

M. Frey, I. Bjelakovic and S. Stanczak (2021). Over-The-Air Computation in Correlated Channels. *IEEE 2020 Information Theory Workshop (ITW), April 11-15, 2021*

D. Schäufele, M. Kasparick, J. Schwardmann, J. Morgenroth and S. Stanczak (2021). Terminal-Side Data Rate Prediction For High-Mobility Users. *IEEE 93rd Vehicular Technology Conference (VTC2021-Spring), Helsinki, Finland, April 2021*

D.F. Külzer, M. Kasparick, A. Palaios, R. Sattiraju, O. D. Ramos-Cantor, D. Wieruch, H. Tchouankem, F. Göttsch, P. Geuer, J. Schwardmann, G. Fettweis, H.D. Schotten and S. Stanczak (2021). AI4Mobile: Use Cases and Challenges of AI-based QoS Prediction for High-Mobility Scenarios. *IEEE Vehicular Technology Conference (VTC Spring) 2021, April 25-28, in Helsinki, Finland*