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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.
Click here [1] for visiting
his website
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 [2]. 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 [3].
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 [4].
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
Download Bibtex Eintrag
[5]
Zurück [6]
Conference,
Symposium, and Workshop Papers
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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
[11]. 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 [12]. 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 [13]. 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 [14]. 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 [15]. 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 [16]. 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 [17]. 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 [18].
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
[19]. 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 [20]. 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 [21].
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 [22]. 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 [23]. 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
[24]. 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 [25]. IEEE
Globecom 2020, December 7-11, in Taipei, Taiwan
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Contact
Dr. Renato L. G. Cavalcante
Fraunhofer Heinrich-Hertz-Institut
Einsteinufer 37
10587 Berlin
E-Mail-Anfrage [31]
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