## 2 Related Work  
In the literature, to maximize ABS coverage, an ABS altitude optimization scheme that considered maximum path loss was proposed in [5]. Similarly, an efficient 3D placement of ABSs was proposed in [10], that aimed at maximizing the coverage by using the minimum transmit power. They decoupled the ABS placement problem into horizontal and vertical dimensions. An optimal placement algorithm was proposed that proved the increase in coverage of users and significant savings in terms of transmit power. A multiple UAVs deployment along with directional antennas was studied in [11], where the authors proved that ABS placement was a function of altitude and antenna gain. Moreover, they also proved that the ABS altitude must be properly adjusted in accordance with the beam width of the antenna to maximize coverage. Results revealed that optimum altitude and location were determined on the basis of the number of UAVs and the beam width of directional antennas. To deal with the problem of traffic congestion, a UAV-aided cellular communication network was presented in [12]. Here, UAVs used reinforcement learning methods to select relay policy for mobile users, when base station was heavily congested. Results showed the reduction in bit error rate and transmission energy. The authors in [13] proposed a coverage enhancement solution for a single UAV, to provide wireless coverage for indoor users during a disaster situation. Their objective was to minimize the total transmit power with maximum path loss using a gradient descent algorithm. A method to optimize the height of a UAV to maximize coverage and minimize outage probability was presented in [14], where they adopted a decode and forward-relaying method. In [15], the authors jointly optimized user association, trajectory, and power for each user to improve data offloading by observing QoS requirements. They decomposed the problem into subproblems and then iteratively solved each problem. The results showed a significant performance gain compared to existing schemes. The authors in [16] presented mobile-edge-computing-assisted UAV communications to enable emergency communications. They addressed the key challenges of user association and resource allocation for energy efficient UAV deployment at edge. To solve this challenge, they adopted RL-assisted resource allocation and user association, which in turn proved beneficial compared to existing solutions. To improve the throughput in overloaded and outage situations, an RL-based ABS 3D deployment approach was proposed in [17], where UAVs found their optimal location to increase system performance gain. Similarly, 3D ABS deployment was presented in [18] to maximize users' coverage by finding ABS optimal altitude and location using a bisection search algorithm. Throughput maximization by adjusting the position of UAV in software-defined disaster areas was presented in [19]. The results showed that a 26% throughput improvement was achieved by optimizing the ABS altitude. In [20], the joint optimization of ABS 3D placement and path loss compensation factor was presented to obtain the ABS maximal coverage. The results improved the coverage by consuming less energy. The deployment of UAVs equipped with intelligent reflecting surface (IRS) was presented in [21] to maximize the sum rate by optimizing the base stations' power allocation, phase shift of the IRS, and horizontal position of UAV using deep reinforcement Learning. The ABS efficient placement as a relay node was studied in [22] with the objective to maximize throughput. They firstly adopted a particle swarm optimization algorithm to identify the optimum ABS location, and then three different approaches were adopted to maximize throughput that involved equal power allocation, water filling, and modified water filling. The results showed that the water filling method gave better results as compared to the other two methods. The deployment of a UAV by optimizing its trajectory to maximize the mean opinion score using a deep Q-learning method was presented in [23].