Correlation Properties in Channels With von Mises-Fisher Distribution of Scatterers
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...
Gaussian Splatting Decoder for 3D-aware Generative Adversarial Networks
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...
Robust mmWave/sub-THz multi-connectivity using minimal coordination and coarse synchronization
How to provide robust connectivity against signal blockage in 6G mmWave/sub-THz networks? This study demonstrates that carefully designed multi-connectivity schemes can realize full macrodiversity gains and significant SNR gains through canonical...
Real-time Experiments towards an Automotive OFDMA Communication Bus
We present a new automotive bus system which allows enhanced channel adaptation and fine-granular multi-user access based on OFDMA. We highlight advantages of OFDMA in automotive applications, sketch the concept and present an FPGA prototype.
Experimental Characterization of a WR3-Coupled Photodiode Transmitter for High-Speed Terahertz Wireless Communication
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...
Quanda: An Interpretability Toolkit for Training Data Attribution Evaluation and Beyond
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...
Multi-Resolution Generative Modeling of Human Motion from Limited Data
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...
SPVLoc: Semantic Panoramic Viewport Matching for 6D Camera Localization in Unseen Environments
We present SPVLoc, a global indoor localization method that accurately determines the six-dimensional (6D) camera pose of a query image and requires minimal scene-specific prior knowledge and no scene-specific training. Our approach employs a...
Causes of Outcome Learning: A causal inference-inspired machine learning approach to disentangling common combinations of potential causes of a health outcome
Nearly all diseases are caused by different combinations of exposures. We present the Causes of Outcome Learning approach (CoOL), which seeks to discover combinations of exposures that lead to an increased risk of a specific outcome in parts of...
Evolution of the 5G New Radio Two-Step Random Access towards 6G Unsourced MAC
This report explores the future of grant-free random access in next-generation 3GPP wireless standards, focusing on how it aligns with the unsourced multiple access channel (UMAC) model.