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Multi-cellular multi-user multi-antenna are at the core of the application-oriented research in the LANEAS group, with the performance an*lysis, precoder design, and topology improvement of multicell mas*ive MIMO, small cells, and heterogeneous networks for A growing activity on BigData is taking place in the LANEAS group, with in particular research in random matrix methods for machine Applications of our theoretical tools are made in the discipline of signal processing for large antenna arrays, such as improved detection At the core of the LANEAS research are theoretical advances in large dimensional random matrix theory, free probabiliy theory, game theory 2017 EURASIP Best Paper Award for the Journal on Wireless Communications and Networking Capacity Scaling of Mas*ive MIMO in Strong Spatial Correlation Regimes Reinforcement Learning Based Capacity Management in Multi-layer Satellite Networks De*p Learning for Energy Efficiency Maximization in Wireless Interference Networks Predictive Deployment of UAV Base Stations in Wireless Networks: Machine Learning Meets Contract Theory Risk-Sensitive Reinforcement Learning for URLLC Traffic in Wireless Networks De*p Learning Based Online Power Control For Large Energy Harvesting Networks Indoor Time Reversal Wireless Communication: Experimental Results for Localization and Signal Coverage De*p Learning-based Method for Channel Calibration in Mas*ive MIMO Systems Impact of Strong Spatial Correlation on the Capacity Scaling of Mas*ive MIMO Small Cell Networks: Deployment, PHY Techniques, and Resource Management Copyright © 2007-2018 Large Networks and Systems Group (LANEAS), Centrale-Supélec. All rights reserved.