# Dynamic Repositioning of Aerial Base Stations for Enhanced User Experience in 5G and Beyond
## 5 Solution Modeling
Moreover, it is evident from the figure that by increasing the grid resolution (number of candidate positions), greater improvement in the total received power is achieved. Furthermore, it can also be observed that maximum improvement is achieved when the number of ABSs is equal to the square root of the grid dimensions: that is \(N\), for a grid of \(N\)-by-\(N\) maximum improvement is achieved when the number of ABSs is \(N\). Figures 5 and 6 show the probability mass function (PMF) of the percentage of users receiving improvement in their received power due to the optimal placement of the ABSs for the Poisson cluster process and Poisson process of user position generation, respectively, whereas Figure 7 shows their cumulative probability distribution function (CCDF). From these figures, it is evident that in the Poisson cluster process, there is more than a 0.8 probability for 60 percent or more users to receive improvement because of optimal placement of ABSs and more than a 0.5 probability for 80 percent or more users. Similarly, for the Poisson process, there is more than a 0.8 probability for 40 percent or more users to receive improvement due to the optimal ABS placements and an approximately 0.4 probability for 60 percent or more users. Figure 4: Percent improvement in received power with respect to the number of Aerial BSs. Figure 5: PMF of the percentage of users receiving performance improvement when user positions were generated as Poisson cluster process. Figure 6: PMF of the percentage of users receiving performance improvement when user positions were generated as Poisson point process. Figure 8 shows the effect of the number of candidate positions on the improvement in received power due to the optimal placement of the ABSs. Increasing the number of candidate positions results in more improvement in the received power. This can be explained by the fact that with more candidate positions, there are more suitable positions for placing the Aerial BSs compared to fewer candidate positions. Figure 8: Effect of the number of candidate positions on improvement in received power. Figure 7: CCDF for percentage of users receiving performance improvement. Figure 9 shows the effect of the number of ABSs on the improvement in the SIR obtained due to optimal placement of the ABSs. It is interesting to note that, unlike the case for received power, as shown in Figure 4, the increase in the number of ABSs leads to a decrease in percent improvement in the SIR. This may be explained by the fact that when the number of ABSs increases, the amount of interference that a user receives also increases, as concluded by some stochastic geometry studies of UDNs [23]; therefore, although the optimal placement still achieves an improvement in the SIR, this improvement is lower compared to the case when there are fewer ABSs. To reduce the effects of increased interference due to the increase in the number of ABSs, one solution can be to assign different channels to neighboring ABSs or use beamforming techniques [24; 25]. Channel allocation and beamforming are, however, out of the scope of this work. Figure 10, just like the case for total received power shown in Figure 8, shows the effect of the number of candidate positions on the improvement in SIR achieved by the optimal placement of the ABSs. As the number of candidate positions increases, the improvement in the received SIR also increases for both user distribution types; however, the rate of increase, in this case, is lower compared to that of the total received power, as shown in Figure 8. The same reasons given in explanation of the results of Figure 9 may be attributed to this observation. Figure 9: Percent improvement in SIR with respect to number of Aerial BSs. ### Discussion

The results presented above support the idea that the dynamic repositioning of ABSs based on user densities can significantly enhance user experience.