# UAV Communication Recovery under Meteorological Conditions
## 5 Conclusions  
This paper proposes a communications recovery strategy for a UAV under meteorological conditions. According to our numerical results, we obtained several conclusions with regard to the communications of the UAV under different weather conditions in four regions. First of all, the percentage reduction in the maximum coverage radius for double or triple reflections compared to one reflection is independent of the frequency when only the atmosphere is considered. Secondly, when considering various weather conditions, relative to Region 1 (suburban), the percentage reduction in the maximum coverage radius of Region 2 to Region 4 (urban, dense urban, and high-rise urban) increases gradually, and the extent of each increase is around 10%.  
Thirdly, based on the condition of the maximum rainfall rate of 100 mm/h, when the frequency is 28 GHz or 71 GHz, the degree of reduction in the coverage area and the optimal altitude of the UAV due to rainfall are the largest among the three weather conditions. When the maximum liquid water density and snowfall rate are set as 0.5 g/m\({}^{3}\) and 10 mm/h, respectively, snow has the smallest effect on the coverage area and optimal height of the UAV when the frequency is 28 GHz, while fog has the least influence on these parameters when the frequency is 71 GHz. This reflects the greater attenuation of the signal at higher frequencies in heavy snow (\(R_{\text{snow}}=10\) mm/h) compared to thick fog (\(R_{\text{fog}}=0.5\) g/m\({}^{3}\)). Lastly, when it rains, the reduction in coverage in the four regions is decreasing in order. The reduction in the coverage area in the suburban environment is the largest (2623.9 m\({}^{2}\)), while it is the smallest (444.9 m\({}^{2}\)) in high-rise urban environments. Therefore, the corresponding time required to restore communication increases in the four regions.  
Regarding the limitations of this work, our ray tracing simulation platform does not take into account scattering and diffraction, and the attenuation models that we use for different weather conditions are not up-to-date models. Therefore, in future research work, we will consider using a more accurate ray tracing simulation platform for simulation, and, secondly, we will study and compare the differences in signal propagation attenuation between existing weather attenuation models. In addition, federated learning can be utilized to optimize the UAV trajectory planning when extreme weather conditions occur [38].  
Figure 20: Time required for the UAV to recover communications under different meteorological conditions.  
**Author Contributions:** Conceptualization, M.S.; methodology, M.S. and Y.H.; software, M.S.; validation, Y.H.; formal analysis, M.S.; writing--original draft preparation, M.S. and Y.H.; writing--review and editing, Y.H., X.D., Z.L. and T.L.; visualization, M.S.; supervision, X.D., Z.L. and T.L. All authors have read and agreed to the published version of the manuscript.  
The work of [PERSON] and [PERSON] was supported in part by the Natural Science Basic Research Project in Shaanxi Province of China under Grants 2020 JM-242 and 2021 JM-185, and in part by the National Natural Science Foundation of China under Grant 61871314. The work of [PERSON], X.D., and [PERSON] was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) under grant RGPIN-2018-03778.  
Not applicable.  
The authors would like to thank the editors, associate editors, and anonymous reviewers for their helpful comments and advice to improve the content and quality of this paper.  
The authors declare no conflict of interest.