## 3 Simulation and Results
### Area-A  
Following a similar approach, the simulation is performed on the entire system of the first area of interest, which is the city center of Hong Kong. The environmental area is chosen to be 1 km\({}^{2}\), with 54 cells located at 18 different locations and 255 SNs. It is found that 137 of the channels are NLoS, 86 are LoS, and 2 SNs are not connected. A CDL channel profile is defined for each SN. Figure 1 illustrates the number of sensors attached to each AP site, and it can be seen that AP 1, 3, 7, 17, and 18 collectively serve 106 SNs, approximately 42% of the sensors. This is because these APs are located in areas where sensor density is high. Underperforming APs with a low SNs association are highlighted in red. The ASE is typically reported in bps/Hz/km\({}^{2}\); however, it is not necessary to account for the area because the simulation is performed over an area of 1 km\({}^{2}\). Equation (8) is used to calculate the ASE, which is found to be 3.417 bps/Hz. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Cluster** & **Cluster PAS** & **Normalized Delay** & **Power in [dB]** & **AoD in [\({}^{\circ}\)]** & **AoA in [\({}^{\circ}\)]** & **ZoD in [\({}^{\circ}\)]** & **ZoA in [\({}^{\circ}\)]** \\ \hline  
\hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._ \begin{table}
\begin{tabular}{c c c} \hline \hline  & **SN-1** & **SN-2** \\ \hline CQI & 2 & 15 \\ Code Rate & 193 & 948 \\ Modulation & ‘QPSK’ & ‘256 QAM’ \\ \hline \hline \end{tabular}
\end{table} 
Table 7: CQI and MCS for channel profiles F and G.  
Underperforming APs with low SNs associations, highlighted in red in Figure 1, are chosen to be replaced by UAVs. Figure 2 shows the relationship between ASE and UAV altitude in our simulation environment. It can be seen that an altitude of 50 m for the UAVs results in a maximum ASE of 6.973 bps/Hz, an improvement of 104% in the ASE compared to using only terrestrial APs. In terms of association, 140 SNs are served by UAVs, and 85 SNs are connected to terrestrial APs. It is also found that one of the remaining terrestrial APs (one site with three cells) does not serve any SN and can be relocated to serve another area. Since the simulation is performed over many SNs, statistical distributions are considered to fit and model different results. Whereas SN distributions in terms of RSS and SINR are modeled according to the normal (Gaussian) distribution, distributions based on CQI and DL throughput are presented in a cumulative ascending manner. The normal (Gaussian) fit shows that the mean of RSS is \(-94.32\) dBm with a standard deviation of 28 dBm for the case of using only terrestrial APs. On the other hand, the mean is found to be \(-63.78\) dBm and the standard deviation is 12 dBm for the case of using UAVs with terrestrial APs. The mean of the SINR fit is found to be \(-0.55\) dB, with a standard deviation of 26 dB for the case of using only terrestrial APs. For the case of using UAVs with terrestrial APs, the mean is improved to 20.69 dB with a standard deviation of 11.6 dB. Interestingly,  
Figure 1: Number of sensors served by each AP site. The red bars show underperforming APs with low SNs association. Figure 2: Area spectral efficiency versus UAV altitude, Area-A. it is found that the absolute means of RSS and SINR are equal to the corresponding means of their normal (Gaussian) fits, with the percentage of error not exceeding 1%. Figure 3 shows the number of SNs reporting each CQI index. When relying solely on terrestrial APs, more than 80 SNs reported the lowest CQI of 1. This means that they are operating at the lowest MCS; meanwhile, they cannot ensure that the BLER does not exceed 0.1. On the other hand, only eight SNs report a CQI index of 1 when the UAVs are considered in conjunction with terrestrial APs. Finally, the performance is evaluated in terms of DL throughput, considering slot-based round-robin scheduling.