### Related Works  
Differing from conventional ground-based communication, air-to-ground (A2G)) communication is subject to the influence of altitude differentials, thus presenting a more complex and dynamic model of the environment [15]. In recent years, many researchers have focused on the design of evaluating indicators, along with communication models in both general and certain environments [16] for air-to-ground channels. To assess the effectiveness and robustness of UAV-assisted networks, various metrics have been proposed for A2G networks. The authors in [17] considered energy efficiency, while [18] considered throughput. In [19], the average completion time of subtasks was applied for assessment. Other evaluating factors include the arrival rate, spectral efficiency, and channel capacity, while factors such as time delay, coverage [20], and outage probability also affect A2G networks. Communication parameters of A2G channels differ in typical scenes involving different environments such as urban, dense urban, suburban, etc. In [21], researchers developed the A2G path loss model in the urban environment, while [22] focused on multilink channel model analysis at 2.4 and 5.9 GHz, both in low altitude circumstance. For aerial sensor networks, [23] introduced a realistic channel model leveraging cooperative UAVs in order to reach maximum spatial exploration efficiency. In [24], the statistical characteristics of the airframe shadowing loss were further analyzed. In [25], the authors gave a spatially and temporally correlated A2G channel model in cellular-connected UAV swarms, as well as a design for performance analysis. Ref. [26] considered atmospheric refractivity and precipitation, and they obtained path loss along the range and altitude. Moreover, [27] proposed a clustering method to analyze time-varying channels. Blessed with auto-mobility and self-decision ability, UAVs can usually perform as aerial base stations in emergency cases when ground base stations are blocked. To execute communication assistance missions, researchers need to design trajectory planning and resource allocation algorithms for UAVs' scheduling. In the field of UAV trajectory planning, researchers have proposed various algorithms and methods to address path-planningproblems in different environments. Heuristic-based methods employ heuristic algorithms such as genetic algorithms, simulated annealing, etc., to find optimal paths. Heuristic-based methods are often suitable for complex environments but may exhibit lower efficiency for large-scale problems. Ref. [28] introduced a 3-D path planning method improved from ant colony optimization, and Ref. [29] searched the UAV configuration space with a modified Mayfly algorithm. For collision avoidance, the slime mold algorithm (SMA) performs well with a design preventing it from local optimization points [30]. Inspired by a genetic algorithm, [31] proposed the ANSGA-III method with enhanced planning ability in complex environments. Graph theory-based methods model the environment as a graph and use classical graph algorithms to find the shortest or optimal paths, which also tend to perform well in simple environments. [PERSON]. [32] discussed an approach of dynamic coloring for UAV planning in emergency cases, and [33] proposed a 3-D deployment method based on [PERSON]'s algorithm, with UAV playing both as an aerial base station and relays. The idea of TSP (Traveling Salesman Problem) was applied in [18] with classified stressed regions, while [34] combined graph theory and convex optimization. Moreover, recent years have witnessed remarkable advancements in path planning facilitated by deep learning methodologies. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have been employed to predict flight trajectories and have shown impressive performance in complex environments. Deep reinforcement learning (DRL) techniques have also been harnessed for this purpose, further contributing to enhanced performance. The trajectory planning method varies with the environment.