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, Cambridge University Press, UK, 2014, 262-303.

Journal Publications

D. A. Awan, 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, 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

Conference, Symposium, and Workshop Papers

A hybrid model-data driven approach for the estimation of the angular power spectrum in massive MIMO systems
Citation key Ren2020SSP
Author R. L. G. Cavalcante and S. Stanczak
Year 2020
Journal IEEE Statistical Signal Processing Workshop 2020, Rio de Janeiro, Brazil, 12th-15th July, 2020 (to appear)
Abstract Information about the angular power spectra (APS) of signals impinging on antenna arrays have important applications in massive MIMO systems such as user clustering, angle-of-arrival estimation, and channel covariance estimation in FDD systems, to cite a few. Current approaches for the estimation of APS can be divided into two main groups: model based methods and data driven methods. The former methods are able to produce reliable estimates with little side information and no training , but they do not exploit any information from datasets (if available) to improve the estimates. In contrast, pure data-driven methods can provided good performance without any knowledge about physical models, but they do not provide any guarantees of robustness against sudden changes in the propagation environment, a common occurrence in wireless systems. Against this background, we propose novel hybrid model and data driven algorithms that use both information about models and any available dataset. To this end, we modify projection and optimization methods for APS estimation by using an inner product (and hence the induced norm and metric) that is learned from data. The proposed algorithms are able to produce reliable estimates that exploit both statistical information available in datasets and model knowledge. As a result, they are able to provide robustness against relatively large changes in the propagation environment without the need to perform frequent training.
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