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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

Adaptive Learning for Symbol Detection
Zitatschlüssel AwanBoo2020
Autor D. A. Awan and R.L.G. Cavalcante and M. Yukawa and S. Stanczak
Buchtitel Machine Learning for Future Wireless Communications
Seiten 15
Jahr 2020
DOI 10.1002/9781119562306.ch11
Ort New York, United States
Monat December
Herausgeber Wiley & IEEE Press
Verlag Wiley & IEEE Press
Kapitel 11
Zusammenfassung This chapter introduces a novel machine learning algorithm for symbol detection in multiuser environments. It considers a challenging multiuser uplink scenario in which the number of antennas available at the base station may be smaller than the number of active users. More specifically, the proposed method is an adaptive (nonlinear) receive filter that learns to detect symbols from data directly, without performing any intermediate estimation tasks (e.g. channel estimation). Furthermore, the method is robust against abrupt changes of the wireless environment. The proposed algorithms for symbol detection are based on the theory of reproducing kernel Hilbert spaces, which have been extensively used in diverse fields such as statistics, probability, signal processing, and machine learning, among others. It also discusses the adaptive learning method for symbol detection in multiuser environments.
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Journal Publications

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. 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. 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. 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. 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. 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 and 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 and M. Yukawa and R. L. G. Cavalcante and A. Dekorsy (2018). Distributed Adaptive Learning with Multiple Kernels in Diffusion Networks. 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. 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. 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 and S. Stanczak and 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 and 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


R. L.G. Cavalcante and M. Kasparick and S. Stanczak (2016). Max-min utility optimization in load coupled interference networks. IEEE Trans. on Wireless Communications


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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