TU Berlin

Institut für TelekommunikationssystemeDr. 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

Distributed Adaptive Learning with Multiple Kernels in Diffusion Networks
Zitatschlüssel DKS01
Autor B.-S. Shin and M. Yukawa and R. L. G. Cavalcante and A. Dekorsy
Seiten 5505-5519
Jahr 2018
Journal IEEE Transactions on Signal Processing
Jahrgang 66
Nummer 21
Monat Nov.
Notiz article in a journal
Herausgeber IEEE
Zusammenfassung We propose an adaptive scheme for distributed learning of nonlinear functions by a network of nodes. The proposed algorithm consists of a local adaptation stage utilizing multiple kernels with projections onto hyperslabs and a diffusion stage to achieve consensus on the estimates over the whole network. Multiple kernels are incorporated to enhance the approximation of functions with several high- A nd low-frequency components common in practical scenarios. We provide a thorough convergence analysis of the proposed scheme based on the metric of the Cartesian product of multiple reproducing kernel Hilbert spaces. To this end, we introduce a modified consensus matrix considering this specific metric and prove its equivalence to the ordinary consensus matrix. Besides, the use of hyperslabs enables a significant reduction of the computational demand with only a minor loss in the performance. Numerical evaluations with synthetic and real data are conducted showing the efficacy of the proposed algorithm compared to the state-of-the-art schemes.
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Conference, Symposium, and Workshop Papers

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)


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


Ismayilov, Rafail and Cavalcante, Renato LG and Stanczak, Slawomir (2021). Deep Learning Based Hybrid Precoding in Dual-Band Communication Systems. ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 4115–4119.


Ismayilov, Rafail and Cavalcante, Renato LG and Stanczak, Slawomir (2021). Deep Learning Beam Optimization in Millimeter-Wave Communication Systems. 2021 IEEE Statistical Signal Processing Workshop (SSP), 581–585.


Komuro, Kei and Yukawa, Masahiro and Cavalcante, Renato LG (2021). Distributed Sparse Optimization With Minimax Concave Regularization. 2021 IEEE Statistical Signal Processing Workshop (SSP), 31–35.


Manjunath, Ramya Panthangi and Schubert, Martin and Cavalcante, RL G and Boban, Mate and Zhou, Chan and Stanczak, Slawomir (2021). Proactive Application Rate Requirement Adaptation Mechanism for Sidelinks. 2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), 1–6.


Agrawal, Navneet and Cavalcante, Renato LG and Stanczak, Slawomir (2021). Adaptive Estimation of Angular Power Spectra for Time-Varying MIMO Channels. 2021 IEEE 22nd International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 96–100.


M. Mehlhose and D. A. Awan and R. L.G. Cavalcante and M. Kurras and S. Stanczak (2020). Machine Learning-Based Adaptive Receive Filtering: Proof-of-Concept on an SDR Platform. accepted, IEEE International Conference on Communications (ICC), Dublin, Ireland, 2020


J. Fink and R. L.G. Cavalcante and S. Stanczak (2020). Online Channel Estimation for Hybrid Beamforming Architectures. ICASSP 2020 - 45th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2020), Barcelona, Spain, 4-8 May 2020


R. L. G. Cavalcante and Q. Liao and S. Stanczak (2020). Connections between spectral properties of asymptotic mappings and solutions to wireless network problems. ICASSP 2020 - 45th International Conference on Acoustics, Speech, and Signal Processing, , May 4-8, 2020 in Barcelona, Spain


R. L. G. Cavalcante and S. Stanczak (2020). Channel covariance estimation in multiuser massive MIMO systems with an approach based on infinite dimensional hilbert spaces. ICASSP 2020 - 45th International Conference on Acoustics, Speech, and Signal Processing, May 4-8, 2020 in Barcelona, Spain


R. L. G. Cavalcante and S. Stanczak (2020). A hybrid model-data driven approach for the estimation of the angular power spectrum in massive MIMO systems. IEEE Statistical Signal Processing Workshop 2020, Rio de Janeiro, Brazil, 12th-15th July, 2020 (to appear)


M. Mehlhose and D. A. Awan and R. L.G. Cavalcante and M. Kurras and S. Stanczak (2020). Machine Learning-Based Adaptive Receive Filtering: Proof-of-Concept on an SDR Platform. 45th International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020, May 4-8, 2020, Barcelona, Spain


R. L.G. Cavalcante and S. Stanczak (2020). Hybrid data and model driven algorithms for angular power spectrum estimation. IEEE GLOBECOM 2020, December 7 - 11, in Taipei, Taiwan


D.F. Külzer and S. Stanczak and R. L.G. Cavalcante and M. Botsov (2020). Predictive Resource Allocation for Automotive Applications using Interference Calculus. IEEE Globecom 2020, December 7-11, in Taipei, Taiwan


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