11 papers
Multi-dimensional hierarchical dictionary search for large MIMO-OFDM systems
Nay Klaimi, Philippe Mary, Luc Le Magoarou
Sparse recovery algorithms are of utmost importance for estimation processes in wireless communications. However, communication systems such as massive multiple input multiple outp…
Unsupervised End-to-End Array Calibration for Multi-Target Integrated Sensing and Communication
José Miguel Mateos-Ramos, Baptiste Chatelier, Luc Le Magoarou +3
In this work, we consider end-to-end calibration of an integrated sensing and communication (ISAC) base station (BS) under gain-phase and antenna displacement impairments without c…
Model-based Implicit Neural Representation for sub-wavelength Radio Localization
Baptiste Chatelier, Vincent Corlay, Musa Furkan Keskin +3
The increasing deployment of large antenna arrays at base stations has significantly improved the spatial resolution and localization accuracy of radio-localization methods. Howeve…
Expressivity of Programmable-Metasurface-Based Physical Neural Networks: Encoding Non-Linearity, Structural Non-Linearity, and Depth
Cheima Hammami, Luc Le Magoarou, Christos Monochristou +4
Wave-based signal processing conventionally encodes input data into the input wavefront, making it challenging to implement non-linear operations. Programmable wave systems enable…
Physically constrained unfolded multi-dimensional OMP for large MIMO systems
Nay Klaimi, Clément Elvira, Philippe Mary +1
Sparse recovery methods are essential for channel estimation and localization in modern communication systems, but their reliability relies on accurate physical models, which are r…
Average and Worst-case Analysis of MIMO Beamforming Loss due to Hardware Impairments
Xuan Chen, Matthieu Crussière, Luc Le Magoarou
In this paper, we investigate the impact of hardware impairments in antenna arrays on the beamforming performance of multi-input multi-output (MIMO) communication systems. We consi…