Aktuelle Publikationen

In this paper, we introduce a novel time domain (TD) correlation based channel sounder that operates in the D-band (110 GHz to 170 GHz) with which we performed dual-polarized sub-THz channel measurements in an industrial environment.

Predicting user locations in dynamic indoor wireless environments is improved by using domain adaptation techniques. This approach effectively addresses challenges such as the birth, death, and drift of scattering clusters caused by environmental...

This paper presents the design and implementation of a novel analog beam former for the frequency range around 85 GHz. The capabilites are demonstrated through an outdoor trial in an urban environment, showcasing the potential of high frequency...

A method to interpret how restart-incremental bidirectional models process local ambiguities, with an analysis on meaning representation and dependency parsing.

We characterize the performance of high symbol rate probabilistic constellation shaping (PCS) in a THz wireless link utilizing a PIN photodiode-assisted transmitter. Using a 64-QAM base constellation we demonstrate high data rate adaptivity,...

The covariance matrix offers a balance between frequent channel estimations and maintaining acceptable system performance. In this work, we propose an intelligent autonomous mechanism that monitors changes in the covariance matrix, minimizing...

This paper reports the development outcomes of the world’s first integrated analog beamforming – transmit and receive – wireless front-ends operating in the D-band (a sub- THz band), particularly designed to operate within 150 GHz to 170 GHz...

Discover a groundbreaking two-timescale method for optimizing the weighted sum-rate in future multi-antenna wireless systems. Our innovative approach overcomes the severe scalability issues of traditional methods, namely the extreme computational...

We propose that deploying Reflecting Intelligent Surfaces (RIS) as auxiliary sensors can either enable (in cases of blockage) or assist UEs in improving sensing performance. This can be achieved by deploying larger surfaces, offering superior...

Efficiently managing network load has long been a challenge in wireless networks, and open radio access networks (O-RAN) can offer a potential solution through open interfaces and optimization capabilities. This paper focuses on addressing the...

We investigated the transmission properties of optical waveguide based on fluorinated acrylate polymer and Ormocer® based polymer in the visible range (VIS). Laser transmission-induced transparency (LTIT) and fluorescence were observed in the...

The paper proposes a class of distributed algorithms where the consensus step is implemented in a scalable and truly decentralized fashion using a novel communication protocol based on the “over-the-air” function computation (OTA-C)...

Heatmaps generated on inputs of image classification networks via explainable AI methods have been observed to resemble segmentations of input images in many cases. We apply the "Right for the Right Reason" paradigm of imposing additional losses...

Neurons in deep neural networks can act polysemantically, meaning that they encode for multiple (unrelated) features. As such, understanding the inner workings of machine learning models becomes more difficult. We present PURE to turn...

While recent studies show high accuracy in the classification of Alzheimer's disease using deep neural networks, the underlying learned concepts have not been investigated. We separated Alzheimer's patients (n=117) from normal controls (n=219) by...

DNNs are prone to relying on spurious correlations in data, posing risks in critical applications. Post-hoc methods exist to mitigate this without retraining but can globally shift latent features distributions, harming model performance. We...

Our new method, AttnLRP, is the first to faithfully and holistically attribute not only input but also latent representations of transformer models with the computational efficiency similar to a single backward pass. We demonstrate that our...

With PCX, we introduce a method that summarizes similar single explanations via prototypical ones. As such, we can understand the whole model behavior quickly and in detail. PCX further allows to validate individual predictions by communicating...

We consider neuromorphic wireless device-edge co-inference for edge intelligence applications. The directed information bottleneck principle is applied to extract the most relevant information for the task. The model demonstrates superior...

Centralized or distributed precoding? This is perhaps the most heated debate in the Cell-free Massive MIMO literature. However, in this work we argue that the best option may actually be a mix of the two. The reason is that centralized precoding,...