## 3 UAV Deployment and Trajectory
### A Static UAV
#### 3.1.1 UAV Bs  
Multiple UAVs are deployed as aerial BSs in UAV wireless networks, and such networks applied for different target purposes such as the coverage maximization and the throughput maximization with the minimum number of UAVs. To achieve wider coverage of the networks, Ref. [21] considers a tactical mission in disaster scenarios and proposes a self-deployment based on the Jaccard distance and artificial intelligence algorithms to serve the maximum number of victims while maintaininga seamless connectivity. Ref. [22] proposes the algorithm to cover a given disaster area with least BSs while satisfying all bandwidth requirements. The algorithm allows for overlapping the coverage area of each UAV, and is tested on both randomly generated map and on the real Hurricane Katrina post-disaster map. Ref. [23] investigates the downlink coverage performance of a drone small cell (DSC) and derives the optimal DSC altitude to maximize the ground coverage and to minimize the required transmit power for a single DSC. This study is extended to two DSCs scenario for a certain geographical area with/without interference considerations. The optimal distance between DSCs is derived for the maximum coverage. The downlink coverage probability of UAVs as a function of the altitude and the antenna gain is derived in [24] to determine the minimum number of UAVs needed to guarantee a target coverage probability for a given geographical area. Authors use the circle packing theory to position UAVs for the maximum coverage area. To improve the throughput and capacity of the networks, ref. [25] utilizes a genetic algorithm (GA) to optimize the position of UAVs, thereby maximizing the fifth-percentile throughput of the network over a given geographical area, and its objective function is represented as  
\[\max_{x_{i},y_{i},v_{i}\in\{1,2,\ldots,N\}}f_{5 th}(x_{i},y_{i}), \tag{6}\]  
where \(f_{5 th}(x_{i},y_{i})\) is the function of the fifth-percentile throughput at the individual locations \((x_{i},y_{i})\) of each UAV \(i\in\{1,2,\ldots,N\}\). Further, Ref. [26] analyzes the throughput gains by exploiting the mobility of UAVs, and then deploys UAV BSs by the brute force search at the optimized locations where both throughput coverage and fifth-percentile spectral efficiency of the network are improved. In [27], the multiple UAVs are deployed to guarantee the required QoS for different user distributions when the co-channel interference exists. First, the mean-shift algorithm and global positioning system (GPS) based prior information of users' positions are utilized to find \(x\)-\(y\) coordinates of UAVs supporting the maximum number of users. Then, the successive convex optimization and block coordinate descent technique are applied to jointly optimize UAVs altitude and transmit power. The two-step algorithm achieves the QoS requirement by maximizing the system throughput for all ground users, thereby obtaining 100% coverage probability and fairness among users. Ref. [28] proposes the distributed algorithm to achieve 100% spectral efficiency of DSCs for a realistic drone speed, height, and user traffic model with dynamic repositioning, but without an additional energy consumption. Ref. [29] utilizes K-means clustering and a stable marriage approach for associations between UAVs and users, and proposes the particle swarm optimization (PSO) based approach to maximize the spectral efficiency considering co-channel interference from other UAVs while guaranteeing the minimum required QoS. Ref. [30] maps UAVs to high demand area by providing solutions to the MBS decision and cooperative UAV allocation problems to increase the capacity and coverage of the wireless communication networks. In [31], the positioning objective is formulated as a minimax facility problem, which is solved by the concept of entropy nets. The proposed placement and distribution of cooperative UAVs in the heterogeneous networks optimize the overall network delay. Ref.