collaborators

6 papers

eess.SP2026

Channel Charting for Position and Orientation

Daniel Richner, Reinhard Wiesmayr, Frederik Zumegen +1

Channel charting (CC) in real-world coordinates is a recently proposed self-supervised machine learning method that maps high-dimensional channel state information (CSI) to user eq…

eess.SP2026

Spatial and Temporal Generalization of CSI-based Neural Positioning

Till-Yannic Müller, Frederik Zumegen, Reinhard Wiesmayr +1

Channel state information (CSI)-based neural positioning learns a mapping from CSI measurements to user equipment (UE) positions using neural networks. However, most existing perfo…

eess.SP2026

Site-Specific Finetuning of Neural Receivers with Real-World 5G NR Measurements

Nuri Berke Baytekin, Reinhard Wiesmayr, Sebastian Cammerer +2

Finetuning wireless receivers to a specific deployment scenario can yield significant error-rate performance improvements without increasing processing complexity. However, site-sp…

eess.SP2025

CSI-Based User Positioning, Channel Charting, and Device Classification with an NVIDIA 5G Testbed

Reinhard Wiesmayr, Frederik Zumegen, Sueda Taner +2

Channel-state information (CSI)-based sensing will play a key role in future cellular systems. However, no CSI dataset has been published from a real-world 5G NR system that facili…

eess.SP2025

Neural Positioning Without External Reference

Till-Yannic Müller, Frederik Zumegen, Reinhard Wiesmayr +2

Channel state information (CSI)-based user equipment (UE) positioning with neural networks -- referred to as neural positioning -- is a promising approach for accurate off-device U…

eess.SP2025

Positioning via Digital-Twin-Aided Channel Charting with Large-Scale CSI Features

José Miguel Mateos-Ramos, José Miguel Mateos-Ramos, Frederik Zumegen +4

Channel charting (CC) is a self-supervised positioning technique whose main limitation is that the estimated positions lie in an arbitrary coordinate system that is not aligned wit…