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

D. A. Awan, R.L.G. Cavalcante, 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, 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, pages 262{303. Cambridge University Press, UK, 2014, 262-303.

Journal Publications

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

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, S. Stanczak, 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, 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

M. Kasparick and R. L. G. Cavalcante and S. Valentin and S. Stanczak and M. Yukawa (2015). Kernel-Based Adaptive Online Reconstruction of Coverage Maps With Side Information. IEEE Transactions on Vehicular Technology, vol. 65, no. 7, pp. 5461-5473, July 2016 (also available at http://arxiv.org/abs/1404.0979)

R. Cavalcante and S. Stanczak and M. Schubert and A. Eisenblätter and U. Türke (2014). Toward Energy-Efficient 5G Wireless Communications Technologies. IEEE Signal Processing Magazine, vol. 31, no. 6, pp. 24-34, Nov. 2014

M. Vasirani, R. Kota, R. L. G. Cavalcante, S. Ossowski and N. R. Jennings (2013). An Agent-Based Approach to Virtual Power Plants of Wind Power Generators and Electric Vehicles. IEEE Transactions on Smart Grid , vol. 4, no. 3, pp. 1314-1322, Sept. 2013

R.L.G. Cavalcante and S. Stanczak (2013). A Distributed Subgradient Method for Dynamic Convex Optimization Problems under Noisy Information Exchange. IEEE Journal of Selected Topics in Signal Processing, vol. 7, no. 2, pp. 243-256, April 2013

R. L. G. Cavalcante, A. Rogers, N. R. Jennings and I. Yamada (2011). Distributed Asymptotic Minimization of Sequences of Convex Functions by a Broadcast Adaptive Subgradient Method. IEEE Journal of Selected Topics in Signal Processing, vol. 5, no. 4, pp. 739-753, Aug. 2011

R. L. G. Cavalcante and B. Mulgrew (2010). Adaptive filter algorithms for accelerated discrete-time consensus. IEEE Trans. Signal Processing, vol. 58, no. 3, pp. 1049-1058, March 2010

R. L. G. Cavalcante, I. Yamada, and B. Mulgrew (2009). An Adaptive Projected Subgradient Approach to Learning in Diffusion Networks. IEEE Trans. Signal Processing,vol. 57, no. 7, pp. 2762-2774, July 2009

R. L. G. Cavalcante and I. Yamada (2009). A flexible peak-to-average power ratio reduction scheme for OFDM systems by the adaptive projected subgradient method. IEEE Trans. Signal Processing, vol. 57, no. 4, pp. 1456-1468, April 2009

T. Piotrowski, R. L. G. Cavalcante, and I. Yamada (2009). Stochastic MV-PURE Estimator–-Robust Reduced-Rank Estimator for Stochastic Linear Model. IEEE Trans. Signal Processing, vol. 57, no. 4, pp. 1293-1303, April 2009

R. L. G. Cavalcante and I. Yamada (2008). Multiaccess interference suppression in orthogonal space-time block coded MIMO systems by the adaptive projected subgradient method. IEEE Trans. Signal Processing, vol. 56, no. 3, pp. 1028-1042, March 2008. (Award Winning Paper)

R. L. G. Cavalcante and I. Yamada (2008). Steady-state analysis of constrained normalized adaptive filters for MAI reduction by energy conservation arguments. Signal Processing, vol. 88, no. 2, pp. 326-338, Feb. 2008

M. Yukawa, R. L. G. Cavalcante, and I. Yamada (2005). Efficient blind MAI suppression in DS/CDMA systems by embedded constraint parallel projection techniques. IEICE Trans. Fundamentals, vol. E88-A, no. 8, pp. 2062-2071, Aug. 2005. (Award Winning Paper)

R. L. G. Cavalcante, I. Yamada, and K. Sakaniwa (2004). A fast blind multiple access interference reduction in DS/CDMA systems based on adaptive projected subgradient method. IEICE Trans. Fundamentals, vol. E87-A, no. 8, pp. 1973-1980, Aug. 2004

Conference, Symposium, and Workshop Papers

Set-Theoretic Learning for Detection in Cell-Less C-RAN System
Citation key Awanset2018
Author D. A. Awan, R. L.G. Cavalcante, Z. Utkovski and S. Stanczak
Year 2018
Journal IEEE Global Conference on Signal and Information Processing, California, USA, Nov. 26-29, 2018
Abstract Cloud-radio access network (C-RAN) can enable cell-less operation by connecting distributed remote radio heads (RRHs) via fronthaul links to a powerful central unit. In the conventional C-RAN, baseband signals are forwarded after quantization/compression to the central unit for centralized processing/detection in order to keep the complexity of the RRHs low. However, the limited capacity of the fronthaul is a significant bottleneck that prevents C-RAN from supporting large systems (e.g. massive machine-type communications (mMTC)). We propose a learning-based C-RAN in which the detection is performed locally at each RRH and, in contrast to the conventional C-RAN, only the likelihood information is conveyed to the central unit. To this end, we develop a general set-theoretic learning method for estimating likelihood functions. Our method can be used to extend existing detection methods to the C-RAN setting.
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