# Data-Driven Analysis of Outdoor-to-Indoor Propagation for 5G Mid-Band Operational Networks
## 3 Experimental Setup and Dataset
### Measurement Campaigns  
Measurement campaigns were carried out in different environments, ranging from outdoor to indoor and deep indoor. Indoor measurements were collected with the receiver in a fixed position and without mobility, while outdoor measurements were collected in different scenarios, i.e., walking, driving, and riding public transportation systems. Measurement campaigns carried out without mobility included a single measurement point, geolocalized using GPS for outdoor campaigns and based on building maps for indoor campaigns. Campaigns carried out in presence of mobility included instead multiple measurement points. At each measurement point, the system simultaneously collected measurements corresponding to different frequency bands (i.e., different network operators), different technologies (i.e., 5G NR, LTE, NB-IoT), and different PCIs. The present analysis focuses on data collected for 5G NR networks in indoor measurements with fixed receiver position and without mobility. Note that no efforts were taken to ensure static environment conditions, e.g., by preventing people from moving in the environment or performing other actions that might affect the propagation environment, such as opening/closing doors and windows. Measurements were thus collected in what are often referred to in the literature as dynamic environment conditions [54]. This choice is justified by the fact that in an outdoor-to-indoor measurement campaign using real world networks the propagation channel is in any case, for the most part, beyond the control of the experimenter. (It is worth observing however that most locations were characterized by a low variability in the indoor propagation environment, due to their positions relative to the 5G Base Station position, often including a window or a single wall obstruction. Furthermore, at the time of measurements there were very few people present in the Department, due to COVID-19-related restrictions.) It is worth observing that static conditions are typically a requirement when a threshold detector is used to identify MPCs, since multiple samples are needed in order to average out spur peaks due to noise; this approach is adopted for example in [54]; however, this is not how the TSMA6 operates: for each PDP returned by the TSMA6, each reported MPC is associated to the successful demodulation of a replica of the signal transmitted by the 5G Base Station for the PCI/SSB pair being considered and the subsequent decoding of the SIB1 information block. This means that the MPC estimation is extremely reliable, as confirmed by the R&S hardware specifications, and no averaging over multiple samples is performed. The measurement campaigns selected for this study were performed at the Department of Information Engineering, Electronics and Telecommunications (DIET) of the Sapienza University of Rome. Table 2 provides details on the characteristics of the building and of its surroundings, as well as on the arrangement of the offices where the indoor measurements took place. Overall, 24 unique campaigns at 8 different locations at DIET (i.e., offices and laboratories) were analyzed in this work. During these campaigns, four 5G operators were detected, including a virtual operator reusing the infrastructure provided by another operator. In order to provide statistically significant results, the analysis focused on the two operators resulting in the highest amount of data samples, called below Op1 and Op2, operating at the carrier frequencies of 3649 MHz and 3725 MHz, respectively. Finally, among all PCIs detected at each location, the analysis focused on those PCIs providing the best coverage at a given location, i.e., for each operator, those PCIs with the highest average ReferenceSignal Received Power (RSRP). Note that RSRP values were computed for each of the SSB that a 5G PCI transmits. Figure 2 shows a Google Earth map of the area surrounding the DIET Department (highlighted in red).