## 5 Discussion, Conclusions, and Outlook  
A setup for automated, high-resolution driving measurements of cellular network performance on highways has been developed and installed. Repeated measurements have been performed on a specific range of the German highway with variations in time, speed, and direction. A data preparation and analysis flow was implemented to obtain and evaluate data with consistent spatial resolution. The presented results for the measurement of RSRP and RSSI indicate significant repeatability and spatial correlation for a chosen provider, which is a prerequisite for a regular, standardized performance assessment. Correlation coefficients above 0.8 were found for different measurements of RSRP and RSSI for the same provider while values lower than 0.8 were found for the correlation for different providers. For RSRQ, the correlation coefficient for non-identical measurements was below 0.5 and different providers could not be distinguished. This can be explained based on the definition: while RSRP and RSSI are basically power measurements, RSRQ additionally depends on the number of available resource blocks which can vary depending on provider, time, and position. Nevertheless, the value is relevant for network performance in terms of data rate or latency. The confidence interval for a confidence level of 0.8 was calculated considering multiple measurements for each location under analysis. The average total width of the confidence interval for RSRP was found to be 13 dBmW to 14 dBmW, which confirms the validity of the measurements performed. The self-assessments of the providers of the respective cellular networks [5,6] have been compared in the exact same locations to the results of this study. Both providers promise a \"very good\" coverage of LTE both outside and inside buildings. In contrast to the self-assessment, the authors cannot confirm a \"best in class\" cellular network. The assessment provided in this study indicates significant areas with a \"risk of disconnect or poor signal\" (Table 6). With respect to the questions of research, presented in Section 1.3, this study contributes as follows:  
1. Does the experimental setup used in this study provide reproducible and consistent measurement results? Yes, the measurements were spatially and temporarily consistent. 2. How can different measurements, with spatial variation in vehicle position along the road under analysis, be compared? The authors implemented a GNSS-based, virtual odometer which provided the base for the vehicle position. The correlation of different measurements indicates a good ability to distinguish between different network operators. 3. What is the level of consistency assessed by the spatial correlation between different measurements with variation in time, speed, and direction? The spatial correlation for different measurements for the same provider was above 0.8, while the correlation coefficient across providers showed values below 0.5. 4. What is the level of consistency based on the confidence per measured position? The confidence intervals for a confidence of 0.8 provided consistent limits across all measurements. However, additional measurements are required for further improvement. Figure 13: Correlation of (**a**) RSRP and RSRQ; (**b**) RSRP and RSSI. The analysis presented indicates that the setup and analysis method is suitable for reliable performance measurements of cellular networks. It allows a detailed assessment to identify weaknesses and develop corrective actions. The authors intend to increase the range of interest to larger distances and cover full highways between German cities. Additionally, parameters like latency or data rate are to be included in the assessment to finally allow a classification of certain routes as suitable for smart logistics. Conceptualization, R.R. and C.N. ; methodology R.R. and C.N. ; software F.F., C.S. and R.R. ; investigation F.F. and C.S. ; resources C.N. and R.R. ; data curation F.F. and C.S. ; writing--original draft preparation R.R.