Several pieces of research were presented on the placement of UAVs. In ^{[6][7][8][9][10]}[6,7,8,9,10], the placement of UAVs for coverage maximization was proposed. Authors in [6] proposed an algorithm that jointly optimizes the 3D UAV placement and path loss compensation factor to maximize the user coverage in the uplink transmission. An approach to minimize the total transmit power required to provide wireless coverage for indoor users was presented in [7]. A placement algorithm that maximizes the number of covered users with minimum transmission power was proposed in [8]. The UAV placement that maximizes the number of served users with different quality-of-service requirements was proposed in [9]. In Reference [10], an analytical approach was used to find the optimum altitude of a UAV for maximum coverage. In References ^{[11][12][13][14]}[11,12,13,14], UAV placement for throughput maximization was proposed. A joint trajectory and resource allocation algorithm for the maximization of the system sum throughput was introduced in [11]. A joint transmit power and trajectory optimization algorithm to maximize the minimum average throughput was proposed in [12]. In Reference [13], the minimum throughput of overall ground users was maximized in the downlink communication by optimizing the scheduling of multi-user communication and association jointly with the trajectory of UAVs and power control. Trajectory and resource allocation are jointly optimized for maximizing the system energy efficiency in [14]. An algorithm to maximize the downlink sum-rate of the network was proposed in [15]. An algorithm for UAV placement based on sparse recovery was presented in [16]. However, all these works consider only the power constraints of the communication link between a UAV and the ground user mobile station (MS) and don’t consider the power constraints of the communication link between a UAV and the BS. Otherwise, researchers that consider both links were presented in ^{[17][18][19][20][21][22][23][24][25]}[17,18,19,20,21,22,23,24,25]. References ^{[17][18][19][20][21]}[17,18,19,20,21] were proposed for throughput maximization. The 3D placement of UAV as a relay station for maximizing the average achievable rate through the one-dimensional linear search was proposed in [17]. In Reference [18], the optimization problem was formulated to maximize the system throughput. An algorithm to find the UAV’s optimal position based on LOS information to maximize the end-to-end throughput was proposed in [19]. Reference [20] explored the relationship between system throughput and the placement of a UAV acting as a communication relay. An approach to jointly optimize throughput and the UAV’s trajectory was presented in [21]. References ^[22][23][22,23] were proposed for data rate maximization. In Reference [22], an algorithm to find the 3D locations of UAVs besides the user-BS associations and bandwidth allocations of the wireless backhaul to maximize the sum logarithmic rate of the users was proposed. Deployment algorithms for deploying a multi-relay network to maximize the end-to-end achievable rate were presented in [23]. An approach to find the optimum altitude of a UAV that minimizes power loss, outage probability, and BER was presented in [24], while an approach to optimize the overall network delays was proposed in [25]. All these works were proposed for UAVs to assist a cellular network. However, the antenna down-tilting and low height of the cellular base station (BS) limits the ability of the UAV relay station to reach high altitudes due to the power constraint on the path between a UAV and a BS [26]. In other words, using the UAV as a relay station in the cellular system makes the UAV lose the advantage of deployment at optimum altitude, which reflects directly on the coverage [10]. Table 1 summarized the pros and cons of related work on UAV placement.