mpNet: variable depth unfolded neural network for massive MIMO channel estimation T Yassine, L Le Magoarou IEEE Transactions on Wireless Communications 21 (7), 5703-5714, 2022 | 16 | 2022 |
The Hexa-X project vision on Artificial Intelligence and Machine Learning-driven Communication and Computation co-design for 6G M Merluzzi, T Borsos, N Rajatheva, AA Benczúr, H Farhadi, T Yassine, ... IEEE Access, 2023 | 11 | 2023 |
Deep learning for location based beamforming with NLOS channels L Le Magoarou, T Yassine, S Paquelet, M Crussière ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and …, 2022 | 11 | 2022 |
Leveraging triplet loss and nonlinear dimensionality reduction for on-the-fly channel charting T Yassine, L Le Magoarou, S Paquelet, M Crussière 2022 IEEE 23rd International Workshop on Signal Processing Advances in …, 2022 | 10 | 2022 |
Channel charting based beamforming L Le Magoarou, T Yassine, S Paquelet, M Crussière 2022 56th Asilomar Conference on Signals, Systems, and Computers, 1185-1189, 2022 | 7 | 2022 |
Optimizing Multicarrier Multiantenna Systems for LoS Channel Charting T Yassine, L Le Magoarou, M Crussière, S Paquelet arXiv preprint arXiv:2310.03762, 2023 | 1 | 2023 |
Model-based Deep Learning for Beam Prediction based on a Channel Chart T Yassine, B Chatelier, V Corlay, M Crussière, S Paquelet, O Tirkkonen, ... 2023 57th Asilomar Conference on Signals, Systems, and Computers, 1636-1640, 2023 | | 2023 |
Cartographie du canal par réduction de dimension et réseaux triplets T Yassine, L Le Magoarou, B Chatelier, S Paquelet, M Crussière | | 2023 |