## 3 Methodology
### ML-Based RSRP and RSRQ Prediction Models  
To develop a realistic prediction model to represent the performance of an LTE communication link based on the capability of the GNet track, RSPR and PRSQ parameters were selected as the parameters to be measured and modeled. RSRP is a key measurement parameter indicating the average received signal power of a single resource element in an LTE resource block (RB) and can be calculated as [17]:  
\[\text{RSRP}[\text{W}]=\frac{1}{N}\sum_{n=1}^{N}P_{n}\, \tag{1}\]  
where \(N\) is the number of received reference signals and \(P_{n}\) is the received power of \(n\)-th reference signal. However, RSRP alone does not fully reflect the quality of the received signal because it also picks up the energy of interfering signals in the corresponding frequency range. Figure 3: Overview of the suburban study environment for the 4G dataset measurement. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Type of BS** & \begin{tabular}{c} **No. of** \\ **Sectors** \\ \end{tabular} & \begin{tabular}{c} **Tilt Angle** \\ **(Degree)** \\ \end{tabular} & \
\begin{tabular}{c} **Height (m)** \\ **Above Ground** \\ **Level** \\ \end{tabular} &
\begin{tabular}{c} **Height (m)** \\ **Above Sea** \\ **Level** \\ \end{tabular} \\ \hline \multirow{2}{*}{**BS A**} & Tower & \multirow{2}{*}{\(3\times 120^{\circ}\)} & \multirow{2}{*}{6} & \multirow{2}{*}{28} & \multirow{2}{*}{99} \\  & located on a hill & & & & \\ \hline \multirow{2}{*}{**BS B**} & Rooftop & \(3\times 120^{\circ}\) & 4 & 11 & 72 \\
**BS C** & Rooftop & \(3\times 120^{\circ}\) & 4 & 23 & 56 \\
**BS D** & Rooftop & \(3\times 120^{\circ}\) & 4 & 12 & 46 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Physical information of the considered BSs. RSRQ is considered another key measurement parameter that indicates the received signal quality level in the LTE network and the effect of interference from adjacent BSs. The RSRQ can be calculated as [17]:  
\[\text{RSRQ}=\text{N}\times\frac{\text{RSRP}[\text{W}]}{\text{RSSI}[\text{W}]}\, \tag{2}\]  
where the reference signal strength indicator (RSSI) is the power measured over the entire bandwidth of occupied RBs, including intracell power, interference, and noise. RSRQ is dimensionless and usually written in dB. Table 3 demonstrates the status of a signal based on its measured RSRP and RSRQ level [35]. The _NumPy_[36] and _Pandas_[37] libraries were used to clean, preprocess, and analyze the collected data statistically. Figure 4 depicts the flowchart of the ML-based RSRP and RSRQ modeling. The raw data consists of a 28,389 \(\times\) 42 matrix, where each row represents one measured sample, including the drone's geographical location, sampling time, and information about the cellular network and received signals. First, the raw data were filtered to extract the measured samples and required features from the desired BSs. In addition, the measurements were cleaned by removing outliers and irregular measurement points. Then, as independent variables, two new columns were added to the dataset, which are 2D distance and elevation angle. The 2D distance, \(d_{2D}\), was measured based on the GPS coordinates of the serving BS and the drone at each sampling point, as [38]:  
\[d_{2D}=2\times\mathcal{R}\times\text{arctan2}\Big{(}\sqrt{a},\ \sqrt{1-a}\Big{)}, \tag{3}\]  
\[a=sin^{2}\Big{(}\frac{lat_{BS}-lat_{\text{LAV}}}{2}\Big{)}+\cos(lat_{\text{BS} })\times\cos(lat_{\text{UAV}})\times\sin^{2}\!\left(\frac{lon_{\text{BS}}-lon_ {\text{UAV}}}{2}\right)\, \tag{4}\]  
where \(\mathcal{R}\) is the Earth's mean radius equal to 6371 km, \(lat_{\text{BS}}\) and \(lon_{\text{BS}}\) are the latitude and longitude of the serving BS in the decimal degrees format, respectively. \(lat_{\text{UAV}}\) and \(lon_{\text{UAV}}\) are the latitude and longitude of the drone in considered sampling point, respectively. Figure 4: Flowchart of the ML-based RSRP and RSRQ modeling.