## 1 Introduction
### Related Work
The authors in [36] analyzed the impact of a UAV's altitude on the sum-rate maximization of a UAV-assisted terrestrial wireless network. The 3D placement of drones to achieve the goal of maximizing the number of ground users served by the drones was investigated in [37]. The minimum number of drones needed for serving all the ground users within a given area was determined in [38]. In [39], evolutionary algorithms were employed to find the optimal placement of low-altitude platforms (LAPs) and portable BSs. The optimal locations and minimum number of UAVs required to completely cover the desired area are investigated for disaster relief scenarios. The authors in [40] determined the optimal location of the UAV by maximizing the average rate while ensuring that the bit error rate does not exceed a specified percentage. The UAVs are categorized as access and gateway UAVs in [41], wherein a disaster scenario, where the terrestrial infrastructure is completely down, was considered. The authors formulated the problem of gateway UAV selection (gateway UAVs use mmWave backhaul) and tried to solve it with multiarm bandit. Different considerations, such as flight time, energy limits, ground user demands, flying regulations, and avoiding NFZs, have a substantial impact on a UAV's trajectory. For maximizing the minimal average rate among ground users, the authors in [42] proposed a simultaneous optimization of user scheduling and UAV trajectory. While a number of jammers with unknown locations sent jamming signals, the authors in [42] presented a combination UAV and ground users' scheduling and transmit power allocation optimization technique. The optimal trajectory of UAVs with multiple antenna for maximum sum-rate in uplink communication was researched [43]. The throughput maximization problem in mobile relaying systems was investigated in [44], where authors optimized the transmit power along with the UAV trajectory considering the practical mobility constraints such as speed and UAV's locations. The authors in [45] proposed an E-Spiral algorithm for accurate photogrammetry. This technique used an energy model to determine different optimal speeds for straight parts of the road, thereby lowering energy consumption and improving the energy model's ability to estimate overall path energy. To characterize the practical path planning requirements of UAVs in difficult situations, the authors in [46] developed an energy-aware, multi-UAV, multiarea coverage path planning model. A bipartite cooperative coevolution (BiCC) algorithm was suggested in this regard, which coevolves interarea and intra-area path planning components to generate good solutions. In [47], the authors proposed a geometric planning-based iterative trajectory optimization technique. To begin, graph theory was used to generate all potential UAV-ground BS association sequences, and candidate association sequences were chosen based on the topological link between UAV and ground BSs. Following that, an iterative handover location design based on the triangle inequality property is given to calculate the shortest flying route with quick convergence and minimal computation complexity. After that, by comparing all of the possible trajectories, the optimal flight trajectory can be determined. The authors presented a tradeoff between mission completion time and flight energy usage [47]. In addition, recent research looks on multiobjective optimization of UAV-assisted communication [48; 49]. Over the course of a flight, a multiobjective optimization problem is constructed to jointly optimize three objectives [49]: (1) maximization of cumulative data rate, (2) maximization of total gathered energy, and (3) reduction of UAV energy consumption. Because these goals are incompatible, the authors suggested an enhanced deep deterministic policy gradient (DDPG) technique for learning UAV control policies with multiple goals.