Aktuelle Publikationen

Dezember 2024

Joint Waveform Design for Communication and Sensing with Adjustable PAPR

Berkan Kilic, Slawomir Stanczak, Martin Kasparick, Kenan Turbic

This paper proposes a dual-functional waveform design for integrated sensing and communication systems under adjustable peak-to-average-power-ratio constraints.


Dezember 2024

Towards Infrastructure-assisted WiFi Rate Adaptation for Converged Networks with Morpheus

Prashiddha Dhoj Thapa, Julius Schulz-Zander, Arne Kappen

The paper discusses a novel framework to integrate heterogeneous Radio Access Technologies (RATs) with the 5G O-RAN for coordination and joint optimization. As an example, it lays out the architecture of a WiFi Radio Intelligent Controller (RIC)...


Dezember 2024

Lossless Decoupling Networks for RF Self-Interference Cancellation in MIMO Full-Duplex Transceivers

Ramez Askar, Wilhelm Keusgen

The article presents a novel self-interference cancellation (SIC) technique using a lossless decoupling network in the RF domain for dedicated transmit-receive antennas. It includes antenna coupling analysis, SISO and 2×2 MIMO tests, and a...


Dezember 2024

Mobilizing the Terahertz Beam: D-Band Analog-Beamforming Front-End Prototyping and Long-Range 6G 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, Benjamin Baumann

This article presents a novel D-band (150-175 GHz) wireless transceiver, achieving 48 dBm EIRP with eight RF beams via a Rotman lens, outperforming prior versions. Developed with InGaAs MMIC technology, it enabled 6G trials, including a 9-meter...


Dezember 2024

Correlation Properties in Channels With von Mises-Fisher Distribution of Scatterers

Kenan Turbic, Slawomir Stanczak, Martin Kasparick

The von Mises-Fisher (vMF) scattering model is widely recognized for characterizing angular power distributions in MIMO systems and demonstrates versatility in modeling both communication and radar channels. This paper introduces exact and simple...


Dezember 2024

Towards Application Level Energy Monitoring for Green 6G Networks

Abishek Dandekar, Julius Schulz-Zander, Johannes Wegener, Ashrafur Rahman

We present a method to monitor network side energy consumption by an application running on the UE by fusing energy monitoring data with in band network telemetry.


November 2024

Gaussian Splatting Decoder for 3D-aware Generative Adversarial Networks

Florian Tim Barthel, Peter Eisert, Anna Hilsmann, Wieland Morgenstern, Arian Beckmann

We present a novel approach that combines the high rendering quality of NeRF-based 3D-aware GANs with the flexibility and computational advantages of 3DGS. By training a decoder that maps implicit NeRF representations to explicit 3D Gaussian...


November 2024

Quanda: An Interpretability Toolkit for Training Data Attribution Evaluation and Beyond

Dilyara Bareeva, Thomas Wiegand, Wojciech Samek, Sebastian Lapuschkin, Anna Hedström, Galip Ümit Yolcu, Niklas Schmolenski

In recent years, training data attribution (TDA) methods have emerged as a promising direction for the interpretability of neural networks. While research around TDA is thriving, limited effort has been dedicated to the evaluation of...


November 2024

Experimental Characterization of a WR3-Coupled Photodiode Transmitter for High-Speed Terahertz Wireless Communication

In-Ho Baek, Patrick Runge, Martin Schell, Felix Ganzer, Colja Schubert, Ronald Freund, Robert Elschner, Oliver Stiewe, Alexander Schindler, Jonas Gläsel, Metin Furkan Ulukan, Trung T. Tran

We analyze the system performance of a large bandwidth photonic THz transmitter based on a WR3 coupled photodiode. Employing probabilistic constellation shaping (PCS) alongside a 64-QAM base constellation, we achieve net data rates of up to 76.8...


November 2024

Multi-Resolution Generative Modeling of Human Motion from Limited Data

David Moreno-Villamarin, Peter Eisert, Anna Hilsmann

We present a generative model that learns to synthesize human motion from limited training sequences. Our framework provides conditional generation and blending across multiple temporal resolutions. moOurdel adeptly captures human motion patterns...


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