**6. Conclusions**

UAVs are expected to soon become a vital part of 5G deployments, acting as both users and aerial BSs. Motivated by the use of UAVs in future 5G deployments, in this paper, we utilize the tools of integral geometry to provide closed-form approximations for UAV blockage probability. In addition to LoS blockage with ground-mounted mmWave BSs, we also considered the case of the operator utilizing rooftop-mounted mmWave BSs.

The numerical results illustrate that the model can closely match the actual UAV LoS blockage probability. Furthermore, the accuracy of approximation increases as either the density of mmWave BSs or the area of interest increases. In analyzing the effect of the rooftop-mounted mmWave BSs, we have shown that one additional rooftop-mounted BS improves the UAV LoS blockage probability as six to twelve ground-mounted mmWave BS. Finally, the most impact on UAV blockage probability is produced by the mmWave BS height, UAV altitude, street width, and mean building block height. The developed model allows for the mathematical assessment of the sought metric for a given deployment condition and density of ground- and rooftop-mounted mmWave BSs.

We foresee two application areas of the proposed model. The first is with regard to system-level simulations, where one needs to utilize simple models for UAV LoS blockage probability. Additionally, the model can be utilized for assessment of the required density of mmWave NR BS to ensure a certain UAV LoS blockage probability. We also note that the

accuracy of the model increases as the deployment area with the homogeneous building deployments increases. Thus, the proposed model needs to be applied to large city districts.

**Author Contributions:** Conceptualization, D.M., V.B. and K.S.; methodology, A.G.; software, V.B.; validation, D.M., A.G., V.B. and K.S.; formal analysis, V.B.; investigation, V.B.; resources, A.G.; data curation, A.G.; writing—original draft preparation, D.M., V.B. and K.S.; writing—review and editing, A.G.; visualization, A.G.; supervision, D.M.; project administration, D.M. and K.S.; funding acquisition, D.M. All authors have read and agreed to the published version of the manuscript.

**Funding:** Sections 3–5 were written by Vyacheslav Begishev under the support of the Russian Science Foundation, project no. 21-79-10139. This paper has been supported by the RUDN University Strategic Academic Leadership Program (recipients Konstantin Samouylov, Sections 1, 2 and 6).

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare that they have no conflicts of interest.
