Aktuelle Publikationen

August 2024

32 GBd, 109 Gbit/s probabilistically shaped THz wireless transmission using PIN-PD based photonic upconversion

In-Ho Baek, Martin Schell, Colja Schubert, Ronald Freund, Robert Elschner, Simon Nellen, Robert Kohlhaas, Oliver Stiewe, Garrit William Johannes Schwanke

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,...


August 2024

Gradual Change Detection in Covariance Matrix: A Lazy Approach

Sida Dai, Lars Thiele, Slawomir Stanczak, Setareh Maghsudi, Ehsan Tohidi

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...


August 2024

Sub-THz D-Band Integrated Analog Beamforming Front-End Prototyping and 6G Outdoor Trials

Ramez Askar, Thomas Haustein, Slawomir Stanczak, Wilhelm Keusgen, Michael Peter, Mathis Schmieder, Thomas Merkle, Jaehoon Chung, Sven Wittig, Laurenz John, Yonghak Suh, Jongpil Lee

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...


August 2024

Two-timescale weighted sum-rate maximization for large cellular and cell-free massive MIMO

Lorenzo Miretti, Slawomir Stanczak, Emil Björnson

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...


August 2024

User-Centric Monostatic Sensing Aided by Reconfigurable Intelligent Surfaces

Abdolvakil Fazli, Slawomir Stanczak, Zoran Utkovski, Ehsan Tohidi

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...


August 2024

Load Balancing in O-RAN

Hammad Zafar, Slawomir Stanczak, Martin Kasparick, Ehsan Tohidi

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...


Juli 2024

First Polymer-based Passive Optical Waveguide for the Visible Range from 633 nm down to 488 nm

Tianwen Qian, Martin Schell, Moritz Kleinert, Crispin Zawadzki, David de Felipe Mesquida, Norbert Keil, Madeleine Weigel, Jakob Reck, Klara Mihov, Martin Kresse, Philipp Winklhofer, Csongor Keuer, Robin Kraft, Thomas Wiglanda, Arne Schleunitz

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...


Juli 2024

Explainable Concept Mappings of MRI: Revealing the Mechanisms Underlying Deep Learning-Based Brain Disease Classification

Christian Tinauer, Wojciech Samek, Sebastian Lapuschkin, Maximilian Dreyer, Reduan Achtibat, Frederik Pahde, Anna Damulina, Maximilian Sackl, Martin Soellradl, Reinhold Schmidt, Stefan Ropele, Christian Langkammer

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...


Juli 2024

Reactive Model Correction: Mitigating Harm to Task-Relevant Features via Conditional Bias Suppression

Dilyara Bareeva, Wojciech Samek, Sebastian Lapuschkin, Maximilian Dreyer, Frederik Pahde

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...


Juli 2024

AttnLRP: Attention-Aware Layer-wise Relevance Propagation for Transformers

Reduan Achtibat, Thomas Wiegand, Wojciech Samek, Sebastian Lapuschkin, Maximilian Dreyer, Sayed M. V. Hatefi, Aakriti Jain

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...


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