Gruner Jonas F, Graßhoff Jan, Wembers Carlos Castelar, Schweppe Kilian, Schildbach Georg, Rostalski Philipp
Institute of Electrical Engineering in Medicine, Universität zu Lübeck, 23562 Lübeck, Germany.
Fraunhofer IMTE, Fraunhofer Research Institution for Individualized and Cell-Based Medical Engineering, 23562 Lübeck, Germany.
Sensors (Basel). 2024 Nov 28;24(23):7601. doi: 10.3390/s24237601.
The advent of 5G technology has facilitated the adoption of private cellular networks in industrial settings. Ensuring reliable coverage while maintaining certain requirements at its boundaries is crucial for successful deployment yet challenging without extensive measurements. In this article, we propose the leveraging of unmanned aerial vehicles (UAVs) and Gaussian processes (GPs) to reduce the complexity of this task. Physics-informed mean functions, including a detailed ray-tracing simulation, are integrated into the GP models to enhance the extrapolation performance of the GP prediction. As a central element of the GP prediction, a quantitative evaluation of different mean functions is conducted. The most promising candidates are then integrated into an informative path-planning algorithm tasked with performing an efficient UAV-based cellular network mapping. The algorithm combines the physics-informed GP models with Bayesian optimization and is developed and tested in a hardware-in-the-loop simulation. The quantitative evaluation of the mean functions and the informative path-planning simulation are based on real-world measurements of the 5G reference signal received power (RSRP) in a cellular 5G-SA campus network at the Port of Lübeck, Germany. These measurements serve as ground truth for both evaluations. The evaluation results demonstrate that using an appropriate mean function can result in an enhanced prediction accuracy of the GP model and provide a suitable basis for informative path planning. The subsequent informative path-planning simulation experiments highlight these findings. For a fixed maximum travel distance, a path is iteratively computed, reducing the flight distance by up to 98% while maintaining an average root-mean-square error of less than 6 dBm when compared to the measurement trials.
5G技术的出现推动了专用蜂窝网络在工业环境中的应用。在工业环境中，确保可靠覆盖并在边界处满足特定要求对于成功部署至关重要，但如果没有广泛的测量则具有挑战性。在本文中，我们提出利用无人机（UAV）和高斯过程（GP）来降低这项任务的复杂性。将包括详细光线追踪模拟的物理信息均值函数集成到GP模型中，以增强GP预测的外推性能。作为GP预测的核心要素，对不同的均值函数进行了定量评估。然后，将最有前景的候选函数集成到一个信息丰富的路径规划算法中，该算法负责基于无人机进行高效的蜂窝网络映射。该算法将物理信息GP模型与贝叶斯优化相结合，并在硬件在环仿真中进行了开发和测试。均值函数的定量评估和信息丰富的路径规划仿真基于德国吕贝克港蜂窝5G-SA校园网络中5G参考信号接收功率（RSRP）的实际测量。这些测量结果作为两次评估的基准事实。评估结果表明，使用适当的均值函数可以提高GP模型的预测精度，并为信息丰富的路径规划提供合适的基础。随后的信息丰富的路径规划仿真实验突出了这些发现。对于固定的最大飞行距离，迭代计算路径，与测量试验相比，飞行距离减少了高达98%，同时平均均方根误差保持在小于6 dBm。
