New use cases for connected vehicles have emerged with the introduction of 5G New Radio, such as tele-operated driving or information sharing from infrastructure-mounted external sensors. Predictive Quality of Service has been recently introduced as a way to improve such use cases. For example, Predictive Quality of Service can inform the autonomous vehicle about upcoming network performance degradation. Machine Learning can be used for Predictive Quality of Service, but a large amount of data with appropriate characteristics is needed to train corresponding models. Simulation-based Machine Learning can help to generate relevant data including a sufficient amount of outlier data. However, uncertainties in the input generation process can affect model evaluation. We designed and implemented a test environment, including a 5G network, in a lab to evaluate the sensitivity of an Machine Learning model to variation in the input generation process of offered data traffic. It was found that the Machine Learning model exhibits different sensitivities against changes in the input data process. We suggest methods from the area of sensitivity analysis for evaluating input-output relations of Machine Learning models with the purpose to identify the impact of parameter uncertainties on the variability of prediction performance. More specifically, these methods can be used to improve confidence in the adoption of Machine Learning model proposals.

Providing reliable low latency wireless links for advanced manufacturing and processing systems is a vision of Industry 4.0. Developing, testing and rating requires accurate models of the radio propagation channel. The current 3rd generation partnership project (3GPP) new radio (NR) model as well as the quasi deterministic radio channel generator (QuaDRiGa) lack the propagation parameters for the industrial indoor scenario. To close this gap, measurements were conducted at 2.37 GHz and 5.4 GHz at operational Siemens premises in Nuremberg, Germany. Furthermore, the campaign was planned to allow the test and parameterization of new features of the QuaDRiGa channel model such as support for device-to-device (D2D) radio links and spatial consistency. A total of 5.9 km measurement track was used to extract the statistical model parameters for line of sight (LOS) and Non-LOS propagation conditions. It was found that the metallic walls and objects in the halls create a rich scattering environment, where a large number of multipath components arrive at the receiver from all directions. This leads to a robust communication link, provided that the transceivers can handle the interference. The extracted parameters can be used in geometric-stochastic channel models such as QuaDRiGa to support simulation studies, both on link and system level.