## 2 Related Works
\begin{table}
\begin{tabular}{c c c c c} \hline
**Ref.** & **Study Focus** & **Modeling Approach** & **Key Findings/Contributions** & **Limitations** \\ \hline
[3],[5,20,21] & & & Demonstrated how drones are served by RS antenna & Simulation-based and not fully reflect real-world \\
[3],[5,20,21] & & & sidesides and revealed the issue of HO when drones & technical challenges and constraints. \\ \cline{3-5}  & Performance evaluation of & & move by the RS antenna sidebook units. & Technical challenges and constraints. \\ \cline{3-5}  & cellular-connected drones & & & The evaluation was based on field measurements, & The evaluations were limited to specific communication \\
[22,23,2] & & & Showed that existing G LTE network could provide & scenarios, such as remote or rural environments. \\ \cline{3-5}  & & & communication links for low-altitude drones. & & Limited to performance evaluation in \\
[24] & Investigate LTE performance for UAV & N/A & Found that the existing LTE network can provide aerial & multipath environments. \\ \cline{3-5}  & & & covering BS. & \\ \hline
[25] & Survey existing and recently developed & & Reviewed recent state-of-the-art ultra-ground channel & Limited to surveying existing models and recent \\  & channel models & & models for different technologies, including cellular-connected UAVs. & developments for channel modeling. \\ \hline
[26] & & Empirical path loss modeling for & The path loss experiments decrease by increasing the & Limited to certain communication scenarios, utilizing \\  & UAV-to-BS channel modeling & & UAV-to-BS scenario. & flight height, approximating free space propagation. & conventional modeling techniques. \\ \hline
[27] & UAV-to-BS channel modeling & & Statistical path loss modeling & The proposed path loss model is a function of the & \\  & UAV-to-BS scenario & & depression angle and the terrestrial coverage beneath & Limited for suburban environments. \\ \hline
[28] & UAV-to-BS RS modeling & ML-based modeling for RS prediction. & - & The proposed method was simply a distance function, \\  & & & RSS prediction. & - & neglecting the effect of parameters such as the UAV’s \\ \hline
[28] & UAV-to-BS RS modeling & & ML-based (ensemble) modeling for & They have utilized multiple ensemble learning methods & Limited to RSS prediction and use little/and \\  & & RSS or SRP prediction. Using nine & to predict the RSS at several heights and presented a & longitude as input features to the models, making it a \\  & & input features. & never ensemble method based on five base learners. & site-specific model. \\ \hline
[30] & UAV-to-BS SRP modeling & ANN for RSBP prediction & Developered two height training methods, the IPB and the CoDE algorithms. Both methods showed favorable & Uses latitude and longitude as input features, making the proposed model side-specific. \\  & & & outcomes, with CoDE-LM achieving the best. & & \\ \hline
[31] & UAV-to-BS SRP and RSRQ modeling & Deep ANN for RSBP and & Results showed that the model performed recently & Limited to one BS in a rural environment, lacking other \\  & & & with a cost function of 0.38 hr training data and & communication scenarios and using limited \\  & & & 0.48B for validation data when predicting RSBP. & training/setting datasets. Use limited and longitude as input features, making the proposed \\  & & & model site-specific. \\ \hline \end{tabular}
\end{table} 
Table 1: A summary of the reviewed UAV-to-BS channel modeling and characterization studies.