5 papers · 1 filter
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…
Active learning for efficient data selection in radio-signal based positioning via deep learning
Vincent Corlay, Milan Courcoux-Caro
We consider the problem of user equipment (UE) positioning based on radio signals via deep learning. As in most supervised-learning tasks, a critical aspect is the availability of…
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…
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…