10 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…
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…
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…
Physically Parameterized Differentiable MUSIC for DoA Estimation with Uncalibrated Arrays
Baptiste Chatelier, José Miguel Mateos-Ramos, Vincent Corlay +4
Direction of arrival (DoA) estimation is a common sensing problem in radar, sonar, audio, and wireless communication systems. It has gained renewed importance with the advent of th…
Model-based learning for multi-antenna multi-frequency location-to-channel mapping
Baptiste Chatelier, Vincent Corlay, Matthieu Crussière +1
Years of study of the propagation channel showed a close relation between a location and the associated communication channel response. The use of a neural network to learn the loc…
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…
Semi-Supervised End-to-End Learning for Integrated Sensing and Communications
José Miguel Mateos-Ramos, Baptiste Chatelier, Christian Häger +3
Integrated sensing and communications (ISAC) is envisioned as one of the key enablers of next-generation wireless systems, offering improved hardware, spectral, and energy efficien…
Model-based learning for location-to-channel mapping
Baptiste Chatelier, Luc Le Magoarou, Vincent Corlay +1
Modern communication systems rely on accurate channel estimation to achieve efficient and reliable transmission of information. As the communication channel response is highly rela…
Influence of Dataset Parameters on the Performance of Direct UE Positioning via Deep Learning
Baptiste Chatelier, Vincent Corlay, Cristina Ciochina +2
User equipment (UE) positioning accuracy is of paramount importance in current and future communications standard. However, traditional methods tend to perform poorly in non line o…
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…