## 6 Conclusions and Future Work  
The proposed concept and demonstrator show the potential of using real-time surrogate GP modeling for efficient online path planning of a drone-based 5G measurement system, opening up the potential for a systematic yet efficient characterization and monitoring of 5G campus networks in all three dimensions. Based on real-world 5G-RSRP measurements in a cellular 5G-SA campus network at the Skandinavienkai of the Port of Lubeck, Germany, we studied the incorporation of prior knowledge into GP mean functions for the scenario of an aerial measurement unit tasked with efficiently mapping a cellular network site. While the results are highly dependent on the test settings, it can be said that the prediction performance can be improved through the use of an appropriate mean prior. If the relation of the training points to the future prediction locations is known, the results of this study can be used to choose the best-fitting GP mean. For prediction locations that cover the same area as the training points, the evaluation results suggest that the distance-based mean (5) is a good choice. If the prediction locations are known to be in a different area than the training points, incorporating as much prior information as possible into the mean is useful. This is the case for the mean prior based on detailed ray-tracing simulations (4). For mobile network sites, this also allows for the training the GP hyperparameters on measurements of one base station to improve the predictions for another base station with few to no measurements. Subsequently, an IPP algorithm with a fixed travel budget using Bayesian optimization was developed and validated in a realistic simulation. The GP mean prior based on detailed ray-tracing simulations (4) can reliably reconstruct the data points from the measurement trial with an average RMSE below 6 dBm and 98 % less distance traveled. The promising performance of the distance-based mean in the evaluations was not observed in the IPP simulations. In both the mean function evaluation and the IPP simulation, the RMSE achieved by the GP models is below the RMSE of two subsequent measurement trials (see Section 2). This highlights a significant limitation of the implemented GPs, i.e., their inability to model temporal changes in signal strength. While time could be integrated as an additional input to the GP, doing so would exponentially increase the computational complexity of hyperparameter learning and the subsequent optimization problem, presenting a practical constraint. The presented IPP algorithm stands out compared to other related approaches [10; 11; 12; 14], as it considers a fixed travel budget, in addition to running online onboard the UAV. Differences in goals and methodology hinder a comparison of its mapping capabilities with those of our approach. As part of a research project, multiple GPs with a simulation-based mean function are used to create a live map visualization of a mobile network site with multiple cell towers. New measurements from continuous terrestrial mobile measurement units are used to update the GP. If no recent data are available, the GP prediction naturally converges to the ray-tracing simulation. The ongoing work also aims at testing the IPP algorithm in real-world settings. Thanks to the ROS framework, a transition to the UAV hardware can easily be achieved once required obstacle avoidance functionality is integrated into the algorithm. This could be achieved, e.g., through the integration of LiDAR or camera sensor data and subsequent restrictions on the search space for the selection of candidate points. Conceptualization, J.F.G. and P.R. ; methodology, J.F.G., J.G. and P.R. ; software, J.F.G., K.S. and J.G. ; validation, J.F.G., J.G. and P.R. ; formal analysis, J.F.G. ; investigation, J.F.G. ; resources, J.F.G. ; data curation, J.F.G. ; writing--original draft preparation, J.F.G. ; writing--review and editing, J.F.G., J.G., C.C.W., G.S. and P.R. ; visualization, J.F.G. ; supervision, P.R. ; project administration, P.R.