# Data-Driven Analysis of Outdoor-to-Indoor Propagation for 5G Mid-Band Operational Networks
## 5 Conclusions  
In this paper, the radio channel characteristics of the 3.5 GHz 5G-NR band were analyzed based on the results of a measurement campaign on deployed commercial 5G networks, performed in a typical urban scenario with the receiver set at multiple indoor locations. A preliminary analysis of the results identified the presence of two operators working with different beamforming strategies, adopting a single wide-beam SSB vs. multiple SSBs.  
In general, the analysis of both time and power-related channel parameters confirmed the behavior observed for 5G signals in previous investigations: the reception of the signal in an urban environment is highly dependent on the building density and the traffic, leading to large variations in the time-related parameters as well as path loss and K-factor values. An exhaustive correlation analysis also confirmed partial results presented in past investigations: a positive correlation between the time-related parameters and PL and the number of paths was observed. The K-factor negatively correlated with time dispersion parameters, PL, and the number of paths.  
The analysis highlighted however marked differences between the two beamforming strategies. The 5G system adopting multiple SSB-based transmissions showed higher RMS delay spread and mean excess delay values as compared to the system employing single wide beam. Furthermore, single wide-beam based transmission experienced a smaller PL than SSB-based transmissions, and the average PL estimate by 3 GPP model is more accurate for wide-beam-based transmission than for SSB beam based transmission. In addition, single wide-beam transmission leads to a higher K-factor and smaller inter-arrival times as compared to multiple SSB-based transmission, although the two beamforming strategies show a similar average number of multipath components arriving at the receiver.  
In conclusion, a single wide-beam beamforming strategy is characterized by better propagation conditions, and promises thus better network performance. While further investigations are required to confirm this trend in different locations and propagation environments, this result is a first indication on the impact that beamforming can have on the performance of a 5G network, and can provide valuable insight in the design of new 5G networks and in the extension of existing ones. This is particularly relevant in light of the expected deployment of standalone 5G NR networks that will allow for a network design specific for 5G NR propagation characteristics.  
Future work will focus on the tuning of a channel model at 3.5 GHz for outdoor-to-indoor propagation based on the dataset partially used in this work and described in [56], and on the design of site-specific channel models, which are recently gaining interest due to the always increasing application of data-driven machine learning approaches to channel propagation modeling and prediction, thanks to the availability in the dataset of both the estimated position of the PCIs and the map of the environment. We would also take into account the channel characterization based on the building material characterization and entry loss.  
Software, U.A.; investigation, U.A. and G.C.; writing--original draft preparation, review, and editing, U.A., G.C., L.D.N. and M.-G.D.B.; review and editing, K.K., M.R., O.A., M.N. and A.B.; project supervision, M.-G.D.B. All authors have read and agreed to the published version of the manuscript. This work was supported by Sapienza University of Rome within research projects with Grants No. RP12117A8483B1C1, RP11916B88A04 AE6 and RP11816433F508D1.  
**Institutional Review Board Statement:** Not applicable.