2 citations · 5 across the 12 of their papers we have counts for
17 papers
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…
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…
CSI Compression using Channel Charting
Baptiste Chatelier, Vincent Corlay, Matthieu Crussière +1
Reaping the benefits of multi-antenna communication systems in frequency division duplex (FDD) requires channel state information (CSI) reporting from mobile users to the base stat…
Efficient Frequency Selective Surface Analysis via End-to-End Model-Based Learning
Cheima Hammami, Lucas Polo-López, Luc Le Magoarou
This paper introduces an innovative end-to-end model-based deep learning approach for efficient electromagnetic analysis of high-dimensional frequency selective surfaces (FSS). Unl…
Unsupervised Learning for Gain-Phase Impairment Calibration in ISAC Systems
José Miguel Mateos-Ramos, Christian Häger, Musa Furkan Keskin +2
Gain-phase impairments (GPIs) affect both communication and sensing in 6G integrated sensing and communication (ISAC). We study the effect of GPIs in a single-input, multiple-outpu…
Model-based Deep Learning for Beam Prediction based on a Channel Chart
Taha Yassine, Baptiste Chatelier, Vincent Corlay +4
Channel charting builds a map of the radio environment in an unsupervised way. The obtained chart locations can be seen as low-dimensional compressed versions of channel state info…
Optimizing Multicarrier Multiantenna Systems for LoS Channel Charting
Taha Yassine, Luc Le Magoarou, Matthieu Crussière +1
Channel charting (CC) consists in learning a mapping between the space of raw channel observations, made available from pilot-based channel estimation in multicarrier multiantenna…
Channel charting based beamforming
Luc Le Magoarou, Taha Yassine, Stephane Paquelet +1
Channel charting (CC) is an unsupervised learning method allowing to locate users relative to each other without reference. From a broader perspective, it can be viewed as a way to…
Efficient Deep Unfolding for SISO-OFDM Channel Estimation
Baptiste Chatelier, Luc Le Magoarou, Getachew Redieteab
In modern communication systems, channel state information is of paramount importance to achieve capacity. It is then crucial to accurately estimate the channel. It is possible to…
Leveraging triplet loss and nonlinear dimensionality reduction for on-the-fly channel charting
Taha Yassine, Luc Le Magoarou, Stéphane Paquelet +1
Channel charting is an unsupervised learning method that aims at mapping wireless channels to a so-called chart, preserving as much as possible spatial neighborhoods. In this paper…
Deep learning for location based beamforming with NLOS channels
Luc Le Magoarou, Taha Yassine, Stéphane Paquelet +1
Massive MIMO systems are highly efficient but critically rely on accurate channel state information (CSI) at the base station in order to determine appropriate precoders. CSI acqui…
mpNet: variable depth unfolded neural network for massive MIMO channel estimation
Taha Yassine, Luc Le Magoarou
Massive multiple-input multiple-output (MIMO) communication systems have a huge potential both in terms of data rate and energy efficiency, although channel estimation becomes chal…
Performance of MIMO channel estimation with a physical model
Luc Le Magoarou, Stéphane Paquelet
Channel estimation is challenging in multi-antenna communication systems, because of the large number of parameters to estimate. One way of facilitating this task is to use a physi…
Massive MIMO channel estimation taking into account spherical waves
Antoine Le Calvez, Luc Le Magoarou, Stéphane Paquelet
Together with millimiter waves (mmWaves), massive multiple-input multiple-output (MIMO) systems are key technological components of fifth generation (5G) wireless communication sys…
Bias-variance tradeoff in MIMO channel estimation
Luc Le Magoarou, Stéphane Paquelet
Channel estimation is challenging in multi-antenna communication systems, because of the large number of parameters to estimate. It is possible to facilitate this task by using a p…
Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)
L. Jacques, C. De Vleeschouwer, Y. Boursier +64
The implicit objective of the biennial "international - Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST) is to foster collaboration between interna…