### Channel Models
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**References** & **Wireless Access** & **Objective** & **Mobility** & **Solution Approach** & **Channel Model** \\ \hline
[22] & Aerial & \
\begin{tabular}{c} Minimizing energy \\ consumption of UAVs \\ \end{tabular} & Static &
\begin{tabular}{c} Optimize UAV positions and \\ functional split options \\ Optimize UT association, \\ transmit power, \\ and UAV placement \\ Optimize travel \\ \end{tabular} & Pathloss \\
[24] & Aerial & \
\begin{tabular}{c} Maximize data throughput \\ \end{tabular} & Mobile &
\begin{tabular}{c} Optimize UAV positions and \\ functional split options \\ Optimize UT association, \\ transmit power, \\ and UAV placement \\ Optimize travel \\ \end{tabular} & Pathloss \\
[25; 26; 27] & Aerial & \
\begin{tabular}{c} Provide cellular connectivity \\ \end{tabular} & Static &
\begin{tabular}{c} Study coverage \\ probability rate \\ \end{tabular} & Stochastic \\
[29] & Aerial and terrestrial & \
\begin{tabular}{c} Extend cellular connectivity & Static &
\begin{tabular}{c} Study coverage probability \\ \end{tabular} & Stochastic \\
[30] & Aerial & \
\begin{tabular}{c} Provide cellular connectivity \\ \end{tabular} & Static &
\begin{tabular}{c} Computers performance of \\ different types of UAVs \\ \end{tabular} & Spatial (two-ray) \\
[31] & Aerial and terrestrial & \
\begin{tabular}{c} Complement terrestrial \\ network in \\ breakdown scenarios \\ \end{tabular} & Static &
\begin{tabular}{c} Private and common \\ message distribution \\ \end{tabular} & Pathloss \\
[13] & Aerial and terrestrial & \
\begin{tabular}{c} Complement terrestrial network \\ Complement terrestrial \\ network and \\ alleviate overload \\ \end{tabular} & Static &
\begin{tabular}{c} UAV proportion and altitude \\ \end{tabular} & Pathloss \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of existing frameworks in UAV-assisted communication systems. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**References** & **Model** & **Frequency Range** & **Type** \\ \hline
[34] &
\begin{tabular}{c} Third Generation Partnership Project \\ (3 GPP) Geometry-based Stochastic \\ Channel Model (GBSCM) \\ Mobile and wireless communications \\ Enablers for the Twenty-twenty \\ Information Society (METIS) \\ Quasi-Deterministic Radio Channel \\ Generator (QuaDRiGa) \\ NYUSIM Channel Model \\ \end{tabular} & 0.5–100 GHz & Geometry-based stochastic \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Channel models supporting mmWave and sub-6 GHz bands. (O2O) and outdoor-to-indoor (O2I). Two stochastic channel models have been defined for 5G link-level evaluations: the clustered delay line (CDL) and the tapped delay line (TDL). Both models have predefined profiles called A, B, C, D, and E. Profiles A, B, and C are for NLoS scenarios, whereas D and E represent LoS channels. Standard channel profiles can be found in [34]. Similar to the traditional 4G or 3G channels, TDL models consider taps with Rayleigh fading characteristics, where delay and power profiles are assigned to each tap. On the other hand, CDL models account for clusters of rays at the transmitter and receiver sides. In addition to delay and power level values, angles are assigned to each cluster. Thus, CDL models are suitable for 3D propagation simulations. The METIS identified 5G requirements for a shorter frequency range, from 2 to 60 GHz, focusing on outdoor square and indoor shopping mall scenarios. The QuaDRiGa supports a frequency range from 0.45 to 100 GHz. However, it is currently well aligned with the TR 36.873 study on the 3D channel model for long-term evolution (LTE) and will be extended to include 5G requirements. The NYUSIM is built on radio propagation measurements for a frequency range from 0.5 to 150 GHz, covering a variety of scenarios as the 3 GPP [33].