Real-Time Algorithms for Combined eMBB and URLLC Scheduling
This paper investigates efficient scheduling algorithms for coexistence scenarios of devices with different quality of service requirements, such as URLLC and eMBB. It proposes a low-complexity algorithm that outperforms current methods, with...
From Hope to Safety: Unlearning Biases of Deep Models via Gradient Penalization in Latent Space
We present a novel method ensuring the right reasons on the concept level by reducing the model's sensitivity towards biases through the gradient. When modeling biases via Concept Activation Vectors, we highlight the importance of choosing robust...
Predictive Handover Optimization
Future networks aim for higher data rates through cell densification, leading to frequent handovers and increased signaling overhead for moving user equipment (UE). This paper presents an optimization scheme using predicted channel state...
Comparison of Sub-THz Radio Channel Characteristics at 158 GHz and 300 GHz in a Shopping Mall Scenario
This paper compares the sub-THz radio channel characteristics at 158 GHz and 300 GHz in a shopping mall scenario by extracting three different path loss models and various channel parameters.
Distributed Fixed-Point Algorithms for Dynamic Convex Optimization over Decentralized and Unbalanced Wireless Networks
Paper studies a class of distributed fixed-point algorithms over truly decentralised and unbalanced graphs, supporting a broad class of communication systems, including a novel OTA computation protocol that implements consensus without any...
Sparse Aperiodic Optical Phased Arrays on Polymer Integration Platform
Solid-state optical beam-steering utilizing polymer waveguides as edge emitters to form optical phased arrays (OPAs) with aperiodic spacing for operation at 1550 nm is demonstrated for the first time. Power consumption of 1.28 mW/? per channel is...
Efficient and Accurate Hyperspectral Image Demosaicing with Neural Network Architectures
This study investigates the effectiveness of neural network architectures in hyperspectral image demosaicing. We introduce a range of network models and modifications, and compare them with classical interpolation methods and existing reference...
Multispectral Stereo-Image Fusion for 3D Hyperspectral Scene Reconstruction
We present a novel approach combining two calibrated multispectral real-time capable snapshot cameras, covering different spectral ranges, into a stereo-system. Therefore, a hyperspectral data-cube can be continuously captured. The combined use...
Multi-View Inversion for 3D-aware Generative Adversarial Networks
Our method builds on existing state-of-the-art 3D GAN inversion techniques to allow for consistent and simultaneous inversion of multiple views of the same subject. We employ a multi-latent extension to handle inconsistencies present in dynamic...
Generative Texture Super-Resolution via Differential Rendering
We propose a generative deep learning network for texture map super-resolution using a differentiable renderer and calibrated reference images. Combining a super-resolution generative adversarial network (GAN) with differentiable rendering, we...