Luc Le Magoarou

Luc Le Magoarou

Associate Professor

INSA Rennes

I am an associate professor in the electronics and telecommuniciations (E&T) department at the national institute of applied sciences (INSA Rennes), France. My research interests include signal processing, machine learning and applied mathematics for efficient and frugal wireless communication systems. For a brief introduction to my research activities, see this talk.

Interests
  • Signal Processing
  • Machine Learning
  • Applied Mathematics
  • Wireless Communications
Education

  • PhD in signal and image processing, 2016

    INSA Rennes / Inria

  • MEng in telecommunications, 2013

    INSA Rennes

News

New preprint on FSS analysis
A new preprint entitled Efficient Frequency Selective Surface Analysis via End-to-End Model-Based Learning just appeared on arXiv. It shows that model-based learning can be used to predict an FSS response efficiently from its geometric parameters, taking advantage of an equivalent circuit representation.
Last updated on Oct 25, 2024 1 min read
New preprint on FSS analysis
New preprint on integrated sensing and communications
A new preprint entitled Unsupervised Learning for Gain-Phase Impairment Calibration in ISAC Systems just appeared on arXiv. It is a collaboration with Henk Wymeersch’s team at Chalmers University which shows that gain and phase impairments can be learnt in a unsupervised manner in the context of ISAC transceivers.
Last updated on Oct 8, 2024 1 min read
New preprint on integrated sensing and communications
Paper accepted to Laser and Photonics Reviews
Our paper entitled Optimal blind focusing on perturbation-inducing targets in sub-unitary complex media has just been accepted to laser and photonics reviews. This is a joint work with Philipp del Hougne and his team, in which a physical model is used to focus waves onto a non-collaborating target, which amounts to perform conjugate beamforming without any pilot exchange.
Last updated on Sep 20, 2024 1 min read
Paper accepted to Laser and Photonics Reviews
New preprint on location-to-channel mapping
A new preprint entitled Model-based learning for multi-antenna multi-frequency location-to-channel mapping just appeared on arXiv. It shows that a physically-inspired neural network can faithfully reconstruct channel responses in a given environment with a reasonable number of learnable weights.
Last updated on Jul 12, 2024 1 min read
New preprint on location-to-channel mapping
Paper accepted to IEEE Transactions on Wireless Communications
Our preprint entitled Optimizing Multicarrier Multiantenna Systems for LoS Channel Charting has just been accepted to IEEE Transactions on Wireless Communications. Congratulations to Taha for this solid piece of work!
Last updated on Jul 12, 2024 1 min read
Paper accepted to IEEE Transactions on Wireless Communications
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Projects

MoBAIWL
Model-Based frugal AI for efficient WireLess communication systems
MoBAIWL

Publications

Quickly discover relevant content by filtering publications.
Cheima Hammami, Lucas Polo-López, Luc Le Magoarou (2024). Efficient Frequency Selective Surface Analysis via End-to-End Model-Based Learning.

Cite arXiv URL

José Miguel Mateos-Ramos, Christian Häger, Musa Furkan Keskin, Luc Le Magoarou, Henk Wymeersch (2024). Unsupervised Learning for Gain-Phase Impairment Calibration in ISAC Systems.

Cite arXiv URL

Jérôme Sol, Luc Le Magoarou, Philipp del Hougne (2024). Optimal Blind Focusing on Perturbation-Inducing Targets in Sub-Unitary Complex Media. Laser & Photonics Reviews.

Cite DOI URL

Baptiste Chatelier, Vincent Corlay, Matthieu Crussière, Luc Le Magoarou (2024). Model-based learning for multi-antenna multi-frequency location-to-channel mapping.

Cite arXiv URL

Taha Yassine, Luc Le Magoarou, Matthieu Crussière, Stéphane Paquelet (2024). Optimizing Multicarrier Multiantenna Systems for LoS Channel Charting. IEEE Transactions on Wireless Communications.

Cite DOI arXiv ieeeXplore

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Students

PhD

Steve Sawadogo (PhD 2023-2026): Distributed MIMO beamforming for secure communications

Kenaz Boa (PhD 2023-2026): Efficient data processing with realistic physical models for reconfigurable intelligent surfaces

Baptiste Chatelier (PhD 2023-2026): Model-based learning for integrated sensing and communication

Taha Yassine (PhD 2020-2024): Model-based learning for channel estimation and charting

Msc

Imane Assoum (Msc 2024): Model-based deep learning for 1D passband signal interpolation

Léon Mykolyshyn (Msc 2024): Model-based deep learning for high-dimensional frequency selective surfaces synthesis

Etienne Rault (Msc 2024): Deep unfolding for channel estimation under IQ imbalance

Cheima Hammami (Msc 2024): Model-based deep learning for efficient electromagnetic modelling of high-dimensional frequency selective surfaces

Amira Bedoui (Msc 2024): Joint channel estimation and precoder design in hybrid MIMO systems