Deep Learning-Based Data Fusion of 6G Sensing and Inertial Information for Target Positioning: Experimental Validation

The sixth-generation (6G) cellular technology will be deployed with a key feature of Integrated Sensing and Communication (ISAC), allowing the cellular network to map the environment through radar sensing on top of providing communication services. In this regard, the entire network can be considered as a sensor with a broader Field of View (FoV) of the environment, assisting in both the positioning of active and detection of passive targets. On the other hand, the non-3GPP sensors available on the target can provide additional information specific to the target that can be beneficially combined with ISAC sensing information to enhance the overall achievable positioning accuracy. In this paper, we first study the performance of the ISAC system in terms of its achievable accuracy in positioning the mobile target in an indoor scenario. Second, we study the performance gain achieved in the ISAC positioning accuracy after fusing the information from the target's non-3GPP sensors. To this end, we propose a novel data fusion solution based on the deep learning framework to fuse the information from ISAC and non-3GPP sensors. We validate our proposed data fusion and positioning solution with a real-world ISAC Proof-of-Concept (PoC) as the wireless infrastructure, an Automated Guided Vehicle (AGV) as the target, and the Inertial Measurement Unit (IMU) sensor on the target as the non-3GPP sensor. The experimental results show that our proposed solution achieves an average positioning error of $3~\textrm{cm}$, outperforming the considered baselines.

Slicing for Dense Smart Factory Network: Current State, Scenarios, Challenges and Expectations

In the era of Industry 4.0, smart factories have emerged as a paradigm shift, redefining manufacturing with the integration of advanced digital technologies. Central to this transformation is the deployment of 5G networks, offering unprecedented levels of connectivity, speed, reliability, and ultra-low latency. Among the revolutionary features of 5G is network slicing, a technology that offers enhanced capabilities through the customization of network resources by allowing multiple logical networks (or slices) to run on top of a shared physical infrastructure. This capability is particularly crucial in the densely packed and highly dynamic environment of smart factories, where diverse applications - from robotic automation to real-time analytics - demand varying network requirements. In this paper, we present a comprehensive overview of the integration of slicing in smart factory networks, emphasizing its critical role in enhancing operational efficiency and supporting the diverse requirements of future manufacturing processes. We elaborate on the recent advances, and technical scenarios, including indoor factory propagation conditions, traffic characteristics, system requirements, slice-aware radio resource management, network elements, enabling technologies and current standardisation efforts. Additionally, we identify open research challenges as well as key technical issues stifling deployments. Finally, we speculate on the future trajectory of slicing-enabled smart factories, emphasizing the need for continuous adaptation to emerging technologies.