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Dr. Renato L. G. Cavalcante
R. L. G. Cavalcante received the electronics engineering degree from the Instituto Tecnologico de Aeronautica (ITA), Brazil, in 2002, and the M.E. and Ph.D. degrees in Communications and Integrated Systems from the Tokyo Institute of Technology, Japan, in 2006 and 2008, respectively. From April 2003 to April 2008, he was a recipient of the Japanese Government (MEXT) Scholarship. He is currently a Research Fellow with the Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute, Berlin, Germany. Previously, he held appointments as a Research Fellow with the University of Southampton, Southampton, U.K., and as a Research Associate with the University of Edinburgh, Edinburgh, U.K.
Dr. Cavalcante received the Excellent Paper Award from the IEICE in 2006 and the IEEE Signal Processing Society (Japan Chapter) Student Paper Award in 2008. He also co-authored the study that received the 2012 IEEE SPAWC Best Student Paper Award. His current interests are in signal processing for distributed systems, multiagent systems, convex analysis, machine learning, and wireless communications.
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Journal Publications
| Zitatschlüssel | Robawan2019 |
|---|---|
| Autor | D. A. Awan and R. L.G. Cavalcante and S. Stanczak |
| Seiten | 68:270-283 |
| Jahr | 2020 |
| DOI | 10.1109/TSP.2019.2959221 |
| Journal | IEEE Transactions on Signal Processing (TSP) |
| Jahrgang | 68 |
| Nummer | 68 |
| Monat | Jan. |
| Zusammenfassung | Learning of the cell-load in radio access networks (RANs) has to be performed within a short time period. Therefore, we propose a learning framework that is robust against uncertainties resulting from the need for learning based on a relatively small training set. To this end, we incorporate prior knowledge about the cell-load in the learning framework. For example, an inherent property of the cell-load is that it is monotonic in downlink (data) rates. To obtain additional prior knowledge we first study the feasible rate region, i.e., the set of all vectors of user rates that can be supported by the network. We prove that the feasible rate region is compact. Moreover, we show the existence of a Lipschitz function that maps feasible rate vectors to cell-load vectors. With these results in hand, we present a learning technique that guarantees a minimum approximation error in the worst-case scenario by using prior knowledge and a small training sample set. Simulations in the network simulator NS3 demonstrate that the proposed method exhibits better robustness and accuracy than standard learning techniques, especially for small training sample sets. |
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Contact
Dr. Renato L. G. CavalcanteFraunhofer Heinrich-Hertz-Institut
Einsteinufer 37
10587 Berlin
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