8 citations · 13 across the 13 of their papers we have counts for
15 papers
ALIGN-FL: Architecture-independent Learning through Invariant Generative component sharing in Federated Learning
Mayank Gulati, Benedikt Groß, Gerhard Wunder
We present ALIGN-FL, a novel approach to distributed learning that addresses the challenge of learning from highly disjoint data distributions through selective sharing of generati…
Machine and Deep Learning for Indoor UWB Jammer Localization
Hamed Fard, Mahsa Kholghi, Benedikt Groß +1
Ultra-wideband (UWB) localization delivers centimeter-scale accuracy but is vulnerable to jamming attacks, creating security risks for asset tracking and intrusion detection in sma…
Tracking UWB Devices Through Radio Frequency Fingerprinting Is Possible
Thibaud Ardoin, Niklas Pauli, Benedikt Groß +3
Ultra-wideband (UWB) is a state-of-the-art technology designed for applications requiring centimeter-level localization. Its widespread adoption by smartphone manufacturer naturall…
Perfectly Secure Key Agreement Over a Full Duplex Wireless Channel
Gerhard Wunder, Axel Flinth, Daniel Becker +1
Secret key generation (SKG) between authenticated devices is a pivotal task for secure communications. Diffie-Hellman (DH) is de-facto standard but not post-quantum secure. In this…
Differentially Private Synthetic Data Generation via Lipschitz-Regularised Variational Autoencoders
Benedikt Groß, Gerhard Wunder
Synthetic data has been hailed as the silver bullet for privacy preserving data analysis. If a record is not real, then how could it violate a person's privacy? In addition, deep-l…
One-Shot Messaging at Any Load Through Random Sub-Channeling in OFDM
Gerhard Wunder, Axel Flinth, Benedikt Groß
Compressive Sensing has well boosted massive random access protocols over the last decade. In this paper we apply an orthogonal FFT basis as it is used in OFDM, but subdivide its i…
A Reverse Jensen Inequality Result with Application to Mutual Information Estimation
Gerhard Wunder, Benedikt Groß, Rick Fritschek +1
The Jensen inequality is a widely used tool in a multitude of fields, such as for example information theory and machine learning. It can be also used to derive other standard ineq…
Guaranteed blind deconvolution and demixing via hierarchically sparse reconstruction
Axel Flinth, Ingo Roth, Benedikt Groß +2
The blind deconvolution problem amounts to reconstructing both a signal and a filter from the convolution of these two. It constitutes a prominent topic in mathematical and enginee…
Explicit CSI Feedback Compression via Learned Approximate Message Passing
Benedikt Groß, Rana Ahmed Salem, Thorsten Wild +1
Explicit channel state information at the transmitter side is helpful to improve downlink precoding performance for multi-user MIMO systems. In order to reduce feedback signalling…
Measure Concentration on the OFDM-based Random Access Channel
Gerhard Wunder, Axel Flinth, Benedikt Groß
It is well known that CS can boost massive random access protocols. Usually, the protocols operate in some overloaded regime where the sparsity can be exploited. In this paper, we…
Hierarchical sparse recovery from hierarchically structured measurements with application to massive random access
Benedikt Groß, Axel Flinth, Ingo Roth +2
A new family of operators, coined hierarchical measurement operators, is introduced and discussed within the well-known hierarchical sparse recovery framework. Such operator is a c…
Hierarchical compressed sensing
Jens Eisert, Axel Flinth, Benedikt Groß +2
Compressed sensing is a paradigm within signal processing that provides the means for recovering structured signals from linear measurements in a highly efficient manner. Originall…
WiFi-Based Channel Impulse Response Estimation and Localization via Multi-Band Splicing
Mahdi Barzegar Khalilsarai, Benedikt Gross, Stelios Stefanatos +2
Using commodity WiFi data for applications such as indoor localization, object identification and tracking and channel sounding has recently gained considerable attention. We study…
Hierarchical Isometry Properties of Hierarchical Measurements
Axel Flinth, Benedikt Groß, Ingo Roth +2
A new class of measurement operators, coined hierarchical measurement operators, and prove results guaranteeing the efficient, stable and robust recovery of hierarchically structur…
Secure Massive IoT Using Hierarchical Fast Blind Deconvolution
Gerhard Wunder, Ingo Roth, Rick Fritschek +2
The Internet of Things and specifically the Tactile Internet give rise to significant challenges for notions of security. In this work, we introduce a novel concept for secure mass…