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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
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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 [7].
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 [8].
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 [9]. 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 [10].
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 [11].
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 [12]. 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 [13]. 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 [14]. 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 [15]. 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 [16]. 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 [17]. 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 [18]. 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 [19]. 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 [20]. 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 [21]. IEEE Trans. on Wireless
Communications
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Conference,
Symposium, and Workshop Papers
Machine Learning-Based Adaptive Receive Filtering:
Proof-of-Concept on an SDR Platform
Citation key |
ICASSPm2020 |
Author |
M.
Mehlhose and D. A. Awan and R. L.G. Cavalcante and M. Kurras and S.
Stanczak |
Year |
2020 |
Journal |
45th
International Conference on Acoustics, Speech, and Signal Processing,
ICASSP 2020, May 4-8, 2020, Barcelona, Spain |
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Contact
Dr. Renato L. G. Cavalcante
Fraunhofer Heinrich-Hertz-Institut
Einsteinufer 37
10587 Berlin
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