## 3 UAV Deployment and Trajectory
### A Static UAV
#### 3.1.1 UAV Bs
[32] investigates the user-demand-based UAV assignment problem over a given geographical area requiring high traffic demands. A neural-based cost function is evaluated to match UAV to a particular geographical area. Ref. [33] investigates the average spectral efficiency depending on the movements of UAV BSs in vertical and horizontal directions. It shows that keeping all UAVs at a fixed altitude outperforms altering the altitude of UAVs flexibly when considering energy consumption on UAV movements. Ref. [34] targets to achieve a load balance in wireless networks. The raw dataset of students' access in wireless network on campus is utilized to predict the number of served users in the area. ARIMA and XGBoost models are applied to linear and non-linear predictions respectively, for the estimation of the number of served uses in the next 24 h. Based on the prediction, UAV BSs are deployed to achieve the load balance. The minimum number of UAVs to construct UAV wireless networks has been derived to manage them efficiently. Ref. [35] proposes a polynomial-time algorithm with successive spiral UAV placement to cover all UEs on the ground, but without considering the height of UAVs. Ref. [36] aims at finding the positions of UAVs and the minimum number of UAVs in target areas with various user densities using a heuristic algorithm. In [37], the miminum number of UAVs is first derived to cover the disaster area by the K-means clustering algorithm considering users' bandwidth constraints. Then, the algorithm for rapid deployment of UAVs is proposed to minimize the maximum deployment delay of UAVs. Even though multiple UAV networks are more reliable than single UAV wireless networks, there have been applications utilizing single UAV wireless networks and the related studies to improve the coverage and the performance along with the efficient management. Ref. [38] proposes an optimal placement algorithm for UAV BS to maximize the number of covered users utilizing the minimum transmit power. Two dimensional deployment problem is decoupled into the vertical and horizontal dimensions, and the UAV BS deployment in the horizontal dimension is modeled as a circle placement problem and a smallest enclosing circle problem. Ref. [39] formulates a three-dimensional placement problem to maximize the number of covered users by UAV, which reduces to a mixed integer non-linear problem (MINLP) by using a one-dimensional bisection search and is solved with the interior point optimizer. The benefit of repositioning UAV BS is explored in [40] to improve the spectral efficiency by placing the UAV BS closer to UEs. The algorithm to autonomously control the repositioning of UAV BS depending on users' activities and movements is proposed, and it achieves 10.5% spectral efficiency gain without an additional energy consumption of UAV. Ref. [41] considers a single UAV to provide a wireless connectivity to indoor users inside a high-rise building under disaster situations. It introduces two cases of practical interest and provides an efficient UAV placement to minimize the total transmit power required to cover the entire high-rise building. Ref. [18] analyzes both power and sum-rate capacity gains of UAV BS and represents the trade-off between them. It proposes an altitude dependent performance model, and determines an optimal altitude range. Within the optimal altitude range, the height of UAV BS is adapted to optimize the sum-rate or the power, respectively.