Channel charting based beamforming. - Luc Le Magoarou; Taha Yassine; Stéphane Paquelet; Matthieu Crussière (IEEECONF, 2022)

Deep Learning for Location Based Beamforming with Nlos Channels. - Luc Le Magoarou; Taha Yassine; Stéphane Paquelet; Matthieu Crussière (ICASSP, 2022)

Leveraging triplet loss and nonlinear dimensionality reduction for on-the-fly channel charting. - Taha Yassine; Luc Le Magoarou; Stéphane Paquelet; Matthieu Crussière (SPAWC, 2022)

mpNet: Variable Depth Unfolded Neural Network for Massive MIMO Channel Estimation. - Taha Yassine; Luc Le Magoarou (IEEE Trans. Wirel. Commun., 2022)

Model-Based Deep Learning for Beam Prediction Based on a Channel Chart. - Taha Yassine; Baptiste Chatelier; Vincent Corlay; Matthieu Crussière; Stéphane Paquelet; Olav Tirkkonen; Luc Le Magoarou (ACSSC, 2023)

The Hexa-X Project Vision on Artificial Intelligence and Machine Learning-Driven Communication and Computation Co-Design for 6G. - Mattia Merluzzi; Tamás Borsos; Nandana Rajatheva; András A. Benczúr; Hamed Farhadi; Taha Yassine; Markus Dominik Mueck; Sokratis Barmpounakis; Emilio Calvanese Strinati; Dilin Dampahalage; Panagiotis Demestichas; Pietro Ducange; et al. (IEEE Access, 2023)

Optimizing Multicarrier Multiantenna Systems for LoS Channel Charting. - Taha Yassine; Luc Le Magoarou; Matthieu Crussière; Stéphane Paquelet (IEEE Trans. Wirel. Commun., 2024)