# A Service-Constrained Positioning Strategy for an Autonomous Fleet of Airborne Base Stations
## 6 Evaluation and Results
### Energy Consumption Evaluation  
In this last subsection, the energy consumption in the three previous non-static scenarios is evaluated. As shown in Section 5, some parameters of a real UAV are needed: the number \(n\) of propellers, the mass \(m\), the propeller blade radius \(r\), the top area \(a\) and the maximum velocities. Moreover, it is important to take into account the required type of UAV: rotary-blade. According to this, in this section, a specific UAV has been selected to implement the energy consumption models. It is the hexacopter DJI Matrice 600 (SZ DJI Technology Co., Ltd., Shenzhen, China) [38], whose parameters are listed in Tables 2 and 3, equipped with six TB48S batteries, which are able to store 129.96 Wh each (equivalent to a total energy of 2807.1 kJ) and a maximum payload capacity of 5.5 kg. Figure 8: Rearrangement of UAVs when subscribers density returns to uniform: positions and coverage areas (**top**); and evolution of the rate per user in each coverage area (**bottom**). Each color in the bottom figure represents one coverage area using the same colors as the ones assigned to letters in the top figure. Regarding the communication payload, although the specific transmission equipment has not been analyzed in this paper, nowadays there exist nano Small Cells that only weights a few kilograms. It is the case, for instance, of the IPaccess nanoLTE E40 (IPaccess, Cambridge, UK) [39]. This BS only weights 1.2 kg, supports long term evolution (LTE) cellular communications with a range of 4 km and is able to radiate the power assumed in this paper, i.e., 25 dBm. Therefore, considering that the BS also needs external batteries, another radio for the backhaul link, etc. the total mass of the transmission equipment has been assumed to be 2 kg, which is supported by the selected UAV. This value is an approximation, but the simulations have proved that in the range going from 1.5 to 2.5 kg, which are the expected values of the payload \(M\), the variations in the consumptions are very low (\(\pm 2\%\)). Once all the parameters of the instantaneous mechanical power model have been detailed, the first experiment consists in evaluating both the vertical and forward models presented in Section 5 for a range of velocities. Figure 9 presents both models using an altitude of 600 m to compute the air density in Equation (49). It is important to take into account that, to show real UAV powers, the factor \(\frac{n}{F_{m}}\) detailed in Equation (37) has been added in both models. By observing this figure, two important points can be extracted. On the one hand, at \(V=0\), both models lead to the same hovering power, which was the expected behavior and, on the other hand, for \(V_{V_{i}}<-1.63\cdot V_{Hov}\), the obtained power is negative, so the energy is extracted from the air through the reverse movement of propellers, which allows recharging the batteries. Finally, as the last step of the energy consumption evaluation, the previous models are applied in the three non-static scenarios presented in the previous section. The energy consumed per UAV over the iterations is detailed in Figure 10, where the horizontal dashed lines show the multiples of the energy of the selected UAV (2807.1 kJ). As can be seen in this figure, each UAV has a different consumption which is related to the maneuvers that have been carried out. Specifically, the highest consumptions belong to the UAVs that have moved fastest in forward flight, which is the expected behavior after analyzing the model shown in Figure 9. In the first non-static scenario, these UAVs are the ones close to the decayed UAV, which are E (black), F (blue) and H (gray), as the new non-served users are assigned to them. Thus, these UAVs need fast displacements to redistribute their big amount of associated users.