2 citations · 4 across the 18 of their papers we have counts for
19 papers
Visualizing Wireless Propagation and Polarization in Augmented Reality with ESPARGOS
Florian Euchner, Stephan ten Brink
Wireless multipath propagation, beamforming, and polarization are central concepts in radio systems, but they are difficult to observe directly because radio-frequency fields are i…
Three-Dimensional Radio Localization: A Channel Charting-Based Approach
Phillip Stephan, Florian Euchner, Stephan ten Brink
Channel charting creates a low-dimensional representation of the radio environment in a self-supervised manner using manifold learning. Preserving relative spatial distances in the…
CSI Obfuscation: Single-Antenna Transmitters Can Not Hide from Adversarial Multi-Antenna Radio Localization Systems
Phillip Stephan, Florian Euchner, Stephan ten Brink
The ability of modern telecommunication systems to locate users and objects in the radio environment raises justified privacy concerns. To prevent unauthorized localization, single…
Integrated Radio Sensing Capabilities for 6G Networks: AI/ML Perspective
Victor Shatov, Steffen Schieler, Charlotte Muth +11
The sixth-generation wireless communications (6G) is often labeled as "connected intelligence". Radio sensing, aligned with machine learning (ML) and artificial intelligence (AI),…
Passive Channel Charting: Locating Passive Targets using Wi-Fi Channel State Information
Florian Euchner, David Kellner, Phillip Stephan +1
We propose passive channel charting, an extension of channel charting to passive target localization. As in conventional channel charting, we follow a dimensionality reduction appr…
ESPARGOS: An Ultra Low-Cost, Realtime-Capable Multi-Antenna WiFi Channel Sounder
Florian Euchner, Tim Schneider, Marc Gauger +1
Multi-antenna channel sounding is a technique for measuring the propagation characteristics of electromagnetic waves that is commonly employed for parameterizing channel models. Ch…
Uncertainty-Aware Dimensionality Reduction for Channel Charting with Geodesic Loss
Florian Euchner, Phillip Stephan, Stephan ten Brink
Channel Charting is a dimensionality reduction technique that learns to reconstruct a low-dimensional, physically interpretable map of the radio environment by taking advantage of…
Channel Charting-Based Channel Prediction on Real-World Distributed Massive MIMO CSI
Phillip Stephan, Florian Euchner, Stephan ten Brink
Distributed massive MIMO is considered a key advancement for improving the performance of next-generation wireless telecommunication systems. However, its efficacy in scenarios inv…
ESPARGOS: Phase-Coherent WiFi CSI Datasets for Wireless Sensing Research
Florian Euchner, Stephan ten Brink
The use of WiFi signals to sense the physical environment is gaining popularity, with some common applications being motion detection and transmitter localization. Standard-complia…
Leveraging the Doppler Effect for Channel Charting
Florian Euchner, Phillip Stephan, Stephan ten Brink
Channel Charting is a dimensionality reduction technique that reconstructs a map of the radio environment from similarity relationships found in channel state information. Distance…
GAN-based Massive MIMO Channel Model Trained on Measured Data
Florian Euchner, Janina Sanzi, Marcus Henninger +1
Wireless channel models are a commonly used tool for the development of wireless telecommunication systems and standards. The currently prevailing geometry-based stochastic channel…
Augmenting Channel Charting with Classical Wireless Source Localization Techniques
Florian Euchner, Phillip Stephan, Stephan ten Brink
Channel Charting aims to construct a map of the radio environment by leveraging similarity relationships found in high-dimensional channel state information. Although resulting cha…
Learning Radio Environments by Differentiable Ray Tracing
Jakob Hoydis, Fayçal Aït Aoudia, Sebastian Cammerer +4
Ray tracing (RT) is instrumental in 6G research in order to generate spatially-consistent and environment-specific channel impulse responses (CIRs). While acquiring accurate scene…
Angle-Delay Profile-Based and Timestamp-Aided Dissimilarity Metrics for Channel Charting
Phillip Stephan, Florian Euchner, Stephan ten Brink
Channel charting is a self-supervised learning technique whose objective is to reconstruct a map of the radio environment, called channel chart, by taking advantage of similarity r…
Deep Learning for Uplink CSI-based Downlink Precoding in FDD massive MIMO Evaluated on Indoor Measurements
Florian Euchner, Niklas Süppel, Marc Gauger +2
When operating massive multiple-input multiple-output (MIMO) systems with uplink (UL) and downlink (DL) channels at different frequencies (frequency division duplex (FDD) operation…
A Distributed Massive MIMO Channel Sounder for "Big CSI Data"-driven Machine Learning
Florian Euchner, Marc Gauger, Sebastian Dörner +1
A distributed massive MIMO channel sounder for acquiring large CSI datasets, dubbed DICHASUS, is presented. The measured data has potential applications in the study of various mac…
Improving Triplet-Based Channel Charting on Distributed Massive MIMO Measurements
Florian Euchner, Phillip Stephan, Marc Gauger +2
The objective of channel charting is to learn a virtual map of the radio environment from high-dimensional CSI that is acquired by a multi-antenna wireless system. Since, in static…
Geometry-Based Phase and Time Synchronization for Multi-Antenna Channel Measurements
Florian Euchner, Phillip Stephan, Marc Gauger +1
Synchronization of transceiver chains is a major challenge in the practical realization of massive MIMO and especially distributed massive MIMO. While frequency synchronization is…
PERIDOT Codes: Replacing Identifiers, Sequence Numbers and Nonces with Permutations
Florian Euchner, Christian Senger
Identifiers and sequence numbers make up a large part of the protocol overhead in certain low-power wide-area networks. The requirement for cryptographic nonces in authentication a…