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 source localization with hardware impairment
A new preprint entitled Physically Parameterized Differentiable MUSIC for DoA Estimation with Uncalibrated Arrays just appeared on arXiv. It is a collaboration with Henk Wymeersch’s team at Chalmers University (Baptiste Chatelier works currently there) which shows that antenna impairments can be learnt while using a subspace method for source localization.
Last updated on Nov 29, 2024 1 min read
New preprint on source localization with hardware impairment
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
See all posts

Projects

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

Publications

Quickly discover relevant content by filtering publications.
Baptiste Chatelier, José Miguel Mateos-Ramos, Vincent Corlay, Christian Häger, Matthieu Crussière, Henk Wymeersch, Luc Le Magoarou (2024). Physically Parameterized Differentiable MUSIC for DoA Estimation with Uncalibrated Arrays.

Cite arXiv URL

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

See all publications

Students

PhD

Nay Klaimi (PhD 2024-2027): Sparse model-based learning for multi-antenna systems

Cheima Hammami (PhD 2024-2027): Arbitrary beam synthesis with compact reconfigurable holographic surfaces

Hamza Mendil (PhD 2024-2027): Signal processing and learning for co-prime antenna arrays

Yara Kafa (PhD 2024-2027): Energy-efficient resource allocation in cell-free NOMA systems

Hussein Abbas (PhD 2024-2027): Resource allocation and beamforming in cell-free massive MIMO systems using channel charting

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