# Performance Evaluation of Direct-Link Backhaul for UAV-Aided Emergency Networks
## 4 Methodology
### Deployment Tool and Algorithms  
The tool used in this paper was an extension of the one used in [5]. The novelty of this tool included modifications to the implementation of LTE radio resource allocation using RB to manage the radio resources and the capacity of the backhaul and the power consumption model for LTE femtocell BSs. The tool, implemented in Java, presents four different phases to achieve the full network simulation.  
First, the tool generates a realistic traffic scenario of the users with a uniform distribution in the studied area, assigns their locations and sets up a facility in the center of the considered area (Step 1 in Figure 3). Second, a list of possible UABS locations is assigned above of each user in the network at the configured flight altitude. Then, for each user, this list is organized based on the signal-to-noise-ratio (SNR) of the link between the user and the UABS locations. Then, for each link, it evaluates the access-link viability by calculating if the SNR is sufficient for the modulation and coding schemes (MCS) of the technology. For the UABS location with the best SNR, the tool asks if this UABS is a new one or an active one. If it is a new one, the algorithm evaluates the backhaul-link viability by checking the SNR and the MCS similarly to the access-link. If some of the links are not viable, it continues to the next UABS possible location and repeats the link evaluation process (Step 2 in Figure 3). Third, after evaluating the access and backhaul link viabilities, it proceeds to assess based on the capacity constraints. The algorithm calculates if the available backhaul resource blocks are enough for the new bit rate of the UABS. If the link capacity is feasible, the user is assigned to that UABS and finally, the used network capacity and power usage values are updated (Step 3 in Figure 3). Unlike [44], where there is a fixed number of UABS, our algorithm assigns UABS when they are needed, based on the restriction of the facility. Fourth, each new user is subject to the optimization of the active UABS to reduce transmission power, minimize flight time of the UABS and reduce the number of RB used. The algorithm finishes when all users are served or when the RBs are fully utilized (Step 4 in Figure 3). Finally, the tool collects the result variables and prints them for easy analysis.  
To achieve stability in the simulation results, we ran the tool and obtained stability in 65 runs per scenario. The number of runs where stability was found depends on the deviation of the averaged values compared to the average values of five investigated values in 100 runs (number of locations,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Drone** & **Type 1** & **Type 2** & **Type 3 Hybrid** \\ \hline Average UAV speed (m/s) & 15 & 12 & 15 \\ UAV battery capacity (Ah) & 2 & 17.33 & 100 \\ UAV battery voltage (V) & 14.3 & 22.2 & 48.0 \\ UAV average usage (A) & 5 & 13 & 25 \\ UAV average usage (W) & 71.3 & 288.6 & 1200 \\ Average Max Fight Time (s) & 900 & 2400 & 7200 \\ Fly height & Uniformly distributed between 20 m and 200 m \\ \hline \hline \end{tabular}
\end{table}
Table 3: Drone specification for simulation.  
number of users, number of drones, mean flight time and mean capacity). We defined stability when the average value of the variables in specific runs had a deviation of \(\pm 0.5\%\) of the average value in 100 runs. We looked for the number of runs for all values that fulfilled this criterion and we found it to be 65.