TU Berlin

Department of Telecommunication SystemsDr. Renato L. G. Cavalcante

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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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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. 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.


Journal Publications

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


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.


Miretti, Lorenzo and Cavalcante, Renato Luis Garrido and Stanczak, Slawomir (2021). Channel Covariance Conversion and Modelling Using Infinite Dimensional Hilbert Spaces. IEEE Transactions on Signal Processing. IEEE, 3145–3159.


D. A. Awan and R. L.G. Cavalcante and S. Stanczak (2020). Robust Cell-Load Learning with a Small Sample Set. IEEE Transactions on Signal Processing (TSP), 68:270-283.


G. Bräutigam, R. L.G. Cavalcante, M. Kasparick, A. Keller and S. Stanczak (2020). AI and open interfaces: Key enablers for campus networks. ITU News Magazine - AI and Machine Learning in 5G, no. 5, p. 55, open access, Dec.


R.L.G. Cavalcante and Q. Liao and S. Stanczak (2019). Connections between spectral properties of asymptotic mappings and solutions to wireless network problems. IEEE Transactions on Signal Processing, Feb. 2019


B.-S. Shin and M. Yukawa and R. L. G. Cavalcante and A. Dekorsy (2018). Distributed Adaptive Learning with Multiple Kernels in Diffusion Networks. IEEE Transactions on Signal Processing, 5505-5519.


B.-S. Shin and M. Yukawa and R. L. G. Cavalcante and A. Dekorsy (2018). Distributed Adaptive Learning with Multiple Kernels in Diffusion Networks. IEEE Transactions on Signal Processing, to appear. Preprint available at arXiv:1801.07087


R.L.G. Cavalcante, M. Kasparick and S. Stanczak (2017). Max-Min Utility Optimization in Load Coupled Interference Networks. IEEE Transactions on Wireless Communications, vol. 16, no. 2, pp. 705-716, Feb. 2017


Qi Liao and R. L. G. Cavalcante (2017). Improving Resource Efficiency with Partial Resource Muting for Future Wireless Networks. Proc. IEEE International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), Oct. 2017


R.L.G. Cavalcante and S. Stanczak and J. Zhang and H. Zhuang (2016). Low complexity iterative algorithms for power estimation in ultra-dense load coupled networks. IEEE Trans. Signal Processing, vol. 64, no. 22, pp. 6058-6070, Nov. 2016


R.L.G. Cavalcante and Y. Shen and S. Stanczak (2016). Elementary Properties of Positive Concave Mappings With Applications to Network Planning and Optimization. IEEE Trans. Signal Processing, vol. 64, no. 7, pp. 1774-1783, April 2016


E. Pollakis and R.L.G. Cavalcante and S. Stanczak (2016). Traffic Demand-Aware Topology Control for Enhanced Energy-Efficiency of Cellular Networks. EURASIP Journal on Wireless Communications and Networks, vol. 2016, no. 1, pp. 1-17, Feb. 2016


R. L.G. Cavalcante and M. Kasparick and S. Stanczak (2016). Max-min utility optimization in load coupled interference networks. IEEE Trans. on Wireless Communications


Conference, Symposium, and Workshop Papers

A hybrid dictionary approach for distributed kernel adaptive filtering in diffusion networks
Citation key Renatodict2018
Author B.-S. Shin and M. Yukawa and R. L. G. Cavalcante and A. Dekorsy
Year 2018
ISBN 978-1-5386-4658-8
ISSN 2379-190X
DOI 10.1109/ICASSP.2018.8461350
Location Calgary, AB, Canada
Journal Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, AB, Canada, 15-20 April 2018
Note Date Added to IEEE Xplore: 13 September 2018
Editor IEEE
Abstract We propose a hybrid dictionary approach for distributed kernel-based adaptive learning of a nonlinear function by a network of nodes. The hybrid dictionary incorporates a local part to improve learning of high frequency components in the function within the local domain of each node and a global part to provide a consensus estimate of the function over the whole region of interest. We apply our scheme to the reconstruction of a spatial distribution by a network of mobile nodes. Performance evaluations show that high frequency components are reconstructed accurately by our hybrid dictionary approach while common schemes are not able to recover them completely.
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