Torsten Braun

This paper introduces Two-Way Ranging-based Cooperative Localization with an Open Ultra-Wideband Dataset from Bern-University (TWR-CLOUD-Bern), a multi-node Ultra-Wideband (UWB) Two-Way Ranging (TWR) dataset and, to our knowledge, the largest open dataset of its kind to date. Collected across 12 scans covering more than 500m2 with 40 Qorvo DWM3001C nodes, the dataset provides ground-truth coordinates, Time of Flight (ToF) measurements, and reception statistics. To support fair, transparent, and reproducible comparisons, we also release a companion evaluation framework. In this framework, 11 scans serve as training partitions for data-driven ranging-model development, while one independent scan with distinct node placements is reserved for testing. The framework fixes node selections and measurement configurations, while varying node counts and locations. It supports single-and multi-sample ranging and covers anchor-free cooperative, anchor-aided cooperative, and anchor-only non-cooperative localization under real-world node-to-node connectivity. The dataset, framework, and documentation are openly available through the Zenodo archival release, making TWR-CLOUD-Bern an immediately usable testbed for UWB localization research.

In emergency rescue scenarios, such as fires where smoke and dust reduce visibility, millimeter-wave (mmWave) radar offers advantages over vision-based localization due to its penetration capability and adaptability to harsh environments. Nevertheless, signal sparsity, multipath interference, and the high cost of data annotation limit its effectiveness in loop closure detection, while conventional trajectory optimization algorithms often lack adaptability, reducing localization accuracy. To address these challenges, this work presents mmDif-DRL, a cooperative and autonomous localization system based on mmWave radar. First, this work proposes a multimodal loop closure detection model by integrating mmWave point clouds and geomagnetic signals. In this model, a Transformer encoder is employed to capture temporal dependencies, and a bidirectional cross-attention mechanism enables effective multimodal fusion. Second, this work proposes a diffusion-based data generation strategy to provide sufficient training samples for the loop closure detection model, while also reducing the need for real data collection and manual annotation. Third, this work proposes a deep reinforcement learning (DRL)-based trajectory optimization strategy, integrated with a Graph-based Simultaneous Localization and Mapping (GraphSLAM) algorithm to achieve robust global optimization. This approach balances adaptability in complex environments with accurate localization. Experiments show that mmDif-DRL achieves an average loop closure detection F1-score of 94.44% and reduces the average localization error to 0.21 m across three indoor scenarios, demonstrating its robustness under challenging conditions.

The growing complexity, scale, and heterogeneity of 6G wireless systems call for a shift toward AI-native architectures that are not only data-driven but also topology-aware, adaptive, and distributed. Graph Neural Networks (GNNs), with their native support for graph-structured data, are well-suited for modeling the irregular and dynamic relationships inherent in wireless communication systems. However, standalone GNNs may be insufficient to address key 6G challenges such as continual learning, data scarcity, and dynamic adaptation. This survey, therefore, explores the emerging synergy between GNNs and complementary AI paradigms, including deep reinforcement learning (DRL), federated learning (FL), meta-learning, generative models, mixture-of-experts (MoE), and world models, enabling hybrid AI–GNN architectures for intelligent control, predictive adaptation, and scalable optimization across the wireless stack. We systematically review how these GNN-centric AI models can support intelligent functionality in next-generation network architectures such as O-RAN, as well as core 6G domains, including edge computing for wireless systems, advanced MIMO, traffic prediction, and digital twins. The survey also highlights key challenges in hybrid AI–GNN adoption, particularly scalability, generalization across dynamic topologies, interpretability, and symbolic reasoning, and discusses emerging strategies such as graph causality learning, dynamic GNNs, and neurosymbolic integration to address them. By consolidating recent advances and outlining open research directions, this work positions hybrid AI–GNN architectures as a promising approach toward enabling intelligent, energy-efficient, and context-aware wireless systems for 6G and beyond.

Transformer-based architectures have introduced end-to-end solutions for Multiple Object Tracking (MOT), seamlessly integrating object detection and association. However, their high computational demands—such as the need for feature map fusion across multiple frames—pose significant challenges to real-time deployment, limiting their practicality. In this paper, we present WT-MOT (Weight Table-based Multiple Object Tracking), a novel framework that addresses these limitations by leveraging the underutilized potential of attention weight tables for efficient object similarity evaluation. WT-MOT employs self- and cross-attention mechanisms to assess object similarity and directly assign identifications, integrating spatial, appearance, and temporal dimensions. By introducing the “frame embedding” concept, WT-MOT enhances the ability to distinguish objects across frames without relying on motion models or post-processing steps. Experimental results on the MOT17 and MOT20 benchmarks demonstrate the effectiveness of WT-MOT, achieving MOTA scores of 76.9% and 73.2%, respectively, setting new performance standards for Transformer-based MOT solutions. These findings highlight WT-MOT as a computationally efficient and robust tool for real-time MOT applications, paving the way for broader adoption of Transformer-based tracking methods in practical environments.

Mobile Internet-of-Things (IoT) applications increasingly demand low-latency service delivery and efficient content dissemination, yet conventional cloud-centric solutions often suffer from excessive backhaul delay.We present an energy-aware floating architecture that enables peer-assisted edge computing and cooperative content caching across mobile IoT devices. The framework comprises three key components: (i) Floating Service, where selected user nodes temporarily act as local servers and adapt in real time to node mobility to sustain seamless service provisioning; (ii) Cooperative Caching, which supports dynamic, proximity-based content exchange among neighboring devices; and (iii) a mobility-aware service-migration predictor that forecasts user movement to guide timely service handover and cache preloading. A radio duty-cycle energy model records transmit, receive, and idle activity to estimate device-side power expenditure. Extensive simulations show that the proposed system reduces end-to-end latency by up to 87.5% for more than 90% of service requests, while the cooperative caching component lowers retrieval delay by up to 58.1% for 28% of communications. Crucially, the floating mechanism increases the average device radio duty cycle by only 1–3 percentage points, corresponding to an incremental power cost of just 3.6–24.6 mW per node. These results underscore the practicality of energy-aware, peer-assisted edge collaboration for next-generation mobile IoT deployments.

Beamforming techniques use massive antenna arrays to formulate narrow Line-of-Sight signal sectors to address the increased signal attenuation in millimeter Wave (mmWave). However, traditional sector selection schemes involve extensive searches for the highest signal-strength sector, introducing extra latency and communication overhead. This paper introduces a dynamic layer-wise and clustering-based federated learning (FL) algorithm for beam sector selection in autonomous vehicle networks called enhanced Dynamic Adaptive FL (eDAFL). The algorithm detects and selects the most important layers of a machine learning model for aggregation in the FL process, significantly reducing network overhead and failure risks. eDAFL also considers intra-cluster and inter-cluster approaches to reduce overfitting and increase the abstraction level. We evaluate eDAFL on a real-world multi-modal dataset, demonstrating improved model accuracy by approximately 6.76% compared to existing methods, while reducing inference time by 84.04% and model size by up to 52.20%.

The next generation of Virtual Reality (VR) applications is expected to provide advanced experiences through Six Degree-of-Freedom (6DoF) technology. However, 6DoF VR applications require latency and throughput guarantees. This article presents a novel intra-domain routing algorithm with throughput guarantees for minimizing the overall end-to-end (E2E) latency for all flows deployed in the network.We investigate the Joint Flow Allocation (JFA) problem to find paths for all flows in a network such that it determines the optimal path for each flow in terms of throughput and latency. The JFA problem is NP-hard. We use a mixed integer linear programming to model the system, along with a heuristic, Flow Latency and Throughput Aware Sensitive Routing (FLATWISE), which is one order of magnitude faster than optimally solving the JFA problem. FLATWISE introduces an adaptive routing approach that dynamically adjusts the path calculation based on E2E latency. The novelty of FLATWISE lies in its unique ability to precisely tune the routing path by either constraining or relaxing the path criteria to align the E2E latency of the selected path with the latency demands of each VR flow. This approach ensures that the latency of the calculated path approximates the latency of each VR flow, enabling more flexible and efficient network routing to meet diverse latency requirements. Our evaluation considers 6DoF VR application flows, which demand high throughput and ultra-low E2E latency. Extensive simulations demonstrate that FLATWISE significantly reduces flow latency, over-provisioned latency, E2E latency, and algorithm execution time when network flows are processed randomly. FLATWISE improves flow throughput and frame rate compared to related work approaches.