Luc Le Magoarou

648 total citations
30 papers, 297 citations indexed

About

Luc Le Magoarou is a scholar working on Electrical and Electronic Engineering, Signal Processing and Artificial Intelligence. According to data from OpenAlex, Luc Le Magoarou has authored 30 papers receiving a total of 297 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Electrical and Electronic Engineering, 10 papers in Signal Processing and 9 papers in Artificial Intelligence. Recurrent topics in Luc Le Magoarou's work include Speech and Audio Processing (8 papers), Indoor and Outdoor Localization Technologies (6 papers) and Wireless Signal Modulation Classification (6 papers). Luc Le Magoarou is often cited by papers focused on Speech and Audio Processing (8 papers), Indoor and Outdoor Localization Technologies (6 papers) and Wireless Signal Modulation Classification (6 papers). Luc Le Magoarou collaborates with scholars based in France, Sweden and Israel. Luc Le Magoarou's co-authors include Rémi Gribonval, Nicolas Tremblay, Stéphane Paquelet, Philipp del Hougne, Jérôme Sol, Alexey Ozerov, Ngoc Q. K. Duong, Matthieu Crussière, Nir Shlezinger and Christian Häger and has published in prestigious journals such as Nature Communications, IEEE Transactions on Signal Processing and IEEE Access.

In The Last Decade

Luc Le Magoarou

26 papers receiving 278 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Luc Le Magoarou France 11 143 108 71 66 41 30 297
Satoshi Takabe Japan 8 149 1.0× 86 0.8× 46 0.6× 65 1.0× 73 1.8× 31 318
S. C. Shrivastava India 7 174 1.2× 101 0.9× 81 1.1× 67 1.0× 31 0.8× 17 317
K. Yamashita Japan 11 259 1.8× 31 0.3× 49 0.7× 30 0.5× 21 0.5× 76 358
Ming Jin China 12 119 0.8× 48 0.4× 37 0.5× 143 2.2× 14 0.3× 55 402
J.S. Fu Singapore 13 237 1.7× 45 0.4× 16 0.2× 284 4.3× 15 0.4× 43 415
Shuaidong Chen China 14 552 3.9× 123 1.1× 14 0.2× 29 0.4× 14 0.3× 109 687
Saeid Haghighatshoar Germany 11 308 2.2× 52 0.5× 72 1.0× 74 1.1× 77 1.9× 31 417
Babak Seyfe Iran 10 162 1.1× 58 0.5× 35 0.5× 38 0.6× 9 0.2× 35 261
Mike Wicks United States 6 181 1.3× 48 0.4× 90 1.3× 523 7.9× 36 0.9× 7 594
M.A. Wickert United States 11 187 1.3× 47 0.4× 93 1.3× 52 0.8× 49 1.2× 66 307

Countries citing papers authored by Luc Le Magoarou

Since Specialization
Citations

This map shows the geographic impact of Luc Le Magoarou's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Luc Le Magoarou with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Luc Le Magoarou more than expected).

Fields of papers citing papers by Luc Le Magoarou

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Luc Le Magoarou. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Luc Le Magoarou. The network helps show where Luc Le Magoarou may publish in the future.

Co-authorship network of co-authors of Luc Le Magoarou

This figure shows the co-authorship network connecting the top 25 collaborators of Luc Le Magoarou. A scholar is included among the top collaborators of Luc Le Magoarou based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Luc Le Magoarou. Luc Le Magoarou is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Häger, Christian, et al.. (2025). Physically Parameterized Differentiable MUSIC for DoA Estimation with Uncalibrated Arrays. arXiv (Cornell University). 3858–3863. 1 indexed citations
2.
Häger, Christian, et al.. (2025). Unsupervised Learning for Gain-Phase Impairment Calibration in ISAC Systems. arXiv (Cornell University). 1–5.
3.
Gast, Arthur de, Luc Le Magoarou, & Nir Shlezinger. (2025). DCD-MUSIC: Deep-Learning-Aided Cascaded Differentiable MUSIC Algorithm for Near-Field Localization of Multiple Sources. SPIRE - Sciences Po Institutional REpository. 1–5. 2 indexed citations
4.
Magoarou, Luc Le, et al.. (2025). Efficient Frequency Selective Surface Analysis via End-to-End Model-Based Learning. arXiv (Cornell University). 1–5. 1 indexed citations
5.
Häger, Christian, et al.. (2024). Semi-Supervised End-to-End Learning for Integrated Sensing and Communications. SPIRE - Sciences Po Institutional REpository. 132–138. 9 indexed citations
6.
Magoarou, Luc Le, Jérôme Sol, George C. Alexandropoulos, et al.. (2024). On the Tacit Linearity Assumption in Common Cascaded Models of RIS-Parametrized Wireless Channels. IEEE Transactions on Wireless Communications. 23(8). 10001–10014. 28 indexed citations
7.
Sol, Jérôme, Luc Le Magoarou, & Philipp del Hougne. (2024). Optimal Blind Focusing on Perturbation‐Inducing Targets in Sub‐Unitary Complex Media. Laser & Photonics Review. 19(2). 8 indexed citations
8.
Sol, Jérôme, et al.. (2024). Experimentally realized physical-model-based frugal wave control in metasurface-programmable complex media. Nature Communications. 15(1). 2841–2841. 21 indexed citations
9.
Crussière, Matthieu, et al.. (2024). CSI Compression Using Channel Charting. SPIRE - Sciences Po Institutional REpository. 1660–1664. 1 indexed citations
10.
Magoarou, Luc Le, et al.. (2024). Model-Based Deep Learning for High-Dimensional Periodic Structures. SPIRE - Sciences Po Institutional REpository. 1–5. 1 indexed citations
11.
Häger, Christian, et al.. (2023). Model-Driven End-to-End Learning for Integrated Sensing and Communication. Chalmers Research (Chalmers University of Technology). 5695–5700. 15 indexed citations
12.
Magoarou, Luc Le, et al.. (2022). mpNet: Variable Depth Unfolded Neural Network for Massive MIMO Channel Estimation. IEEE Transactions on Wireless Communications. 21(7). 5703–5714. 20 indexed citations
13.
Magoarou, Luc Le, et al.. (2022). Leveraging triplet loss and nonlinear dimensionality reduction for on-the-fly channel charting. arXiv (Cornell University). 1–5. 12 indexed citations
14.
Magoarou, Luc Le. (2021). Similarity-Based Prediction for Channel Mapping and User Positioning. IEEE Communications Letters. 25(5). 1578–1582. 10 indexed citations
15.
Magoarou, Luc Le. (2021). Efficient Channel Charting via Phase-Insensitive Distance Computation. IEEE Wireless Communications Letters. 10(12). 2634–2638. 12 indexed citations
16.
Savaux, Vincent & Luc Le Magoarou. (2020). On the Computation of Integrals of Bivariate Gaussian Distribution. 1–6.
17.
Magoarou, Luc Le & Stéphane Paquelet. (2020). Channel Estimation: Unified View of Optimal Performance and Pilot Sequences. IEEE Transactions on Signal Processing. 68. 5588–5601. 3 indexed citations
18.
Bazzi, Samer, Luc Le Magoarou, Mohamad Assaad, et al.. (2019). Exploiting the Massive MIMO Channel Structural Properties for Minimization of Channel Estimation Error and Training Overhead. IEEE Access. 7. 32434–32452. 13 indexed citations
19.
Magoarou, Luc Le, Rémi Gribonval, & Nicolas Tremblay. (2017). Approximate Fast Graph Fourier Transforms via Multilayer Sparse Approximations. IEEE Transactions on Signal and Information Processing over Networks. 4(2). 407–420. 42 indexed citations
20.
Magoarou, Luc Le, Rémi Gribonval, & Alexandre Gramfort. (2015). FAμST: Speeding up linear transforms for tractable inverse problems. abs 1212 3631. 2516–2520. 3 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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