## 2 Related Work  
The use of UAVs in emergency communication networks has received significant attention in recent years [8]. Numerous studies have explored various aspects of deploying UAVs for disaster response, including optimal placement, trajectory planning, and energy efficiency [9]. This section reviews the related work in the areas of UAV-based communication systems, trajectory optimization, energy management, and the application of reinforcement learning in UAV control. UAVs have been extensively studied for their potential to establish temporary communication networks in post-disaster areas. [PERSON] et al. (2023) propose the multi-hop opportunistic 3D routing (MO3 DR) algorithm to address post-disaster routing challenges such as coverage requirements, inter-UAV collision avoidance, and reliable multi-hop routing without trajectory planning. Their simulations validate that maintaining the UAVs within a threshold inter-UAV distance effectively meets the coverage and collision constraints and thus maximizes the expected progress of data toward the terrestrial base station (TBS) [10]. [PERSON] et al. (2023) propose an air-ground cooperation architecture based on an ad hoc UAV network to address the challenges of damaged ground servers in disaster scenarios. They define system cost as a weighted sum of task delay and energy consumption and propose a joint optimization algorithm that iteratively solves the task scheduling and UAV deployment sub-problems. Their simulation results demonstrate that the proposed algorithm significantly reduces task delay and energy consumption while achieving a good trade-off between these metrics for diverse tasks [11]. These studies highlight the importance of UAVs in maintaining communication services when the ground infrastructure is unavailable or damaged. Trajectory optimization is a critical aspect of UAV deployment and directly impacts the efficiency and effectiveness of the communication network. Several approaches have been proposed to address this challenge. For example, [PERSON] et al. (2023) [12] address the trajectory planning problem in their work on joint power and 3D trajectory optimization for UAV-enabled wireless powered communication networks (WPCNs) in the presence of obstacles. They decompose the problem into two sub-problems: power allocation and 3D trajectory optimization. The authors propose an improved non-dominated sorting genetic algorithm-II with a K-means initialization operator and variable dimension mechanism (NSGA-II-KV) for power allocation as well as an improved particle swarm optimization (PSO-NGDP) for trajectory optimization. Their approach effectively increases the number of covered wireless devices, enhances time efficiency, and reduces UAV flight distance, thereby demonstrating significant improvements in the energy utilization efficiency in complex environments [13]. Similarly, [PERSON] et al. [13] introduce a heuristic crossing search-and-rescue optimization algorithm (HC-SAR) for UAV path planning, which integrates a heuristic crossover strategy with a basic SAR algorithm to improve convergence speed and maintain population diversity. The HC-SAR algorithm demonstrates high performance in both two-dimensional and three-dimensional environments, significantly outperforming the traditional algorithms, such as differential evolution (DE) and ant lion optimizer (ALO), in terms of path length and fuel efficiency [14]. The application of DRL in UAV trajectory optimization is a rapidly growing field. [PERSON] et al. (2023) [11] propose an improved PSO algorithm for the energy-efficient path planning of UAVs in mountainous terrain. By integrating a deep deterministic policy gradient (DDPG) model for adaptive parameter tuning, the algorithm significantly enhances the global search capability and avoids local optima. The simulation results demonstrate that this approach effectively reduces the nonessential energy consumption and improves the UAV mission efficiency in complex environments [12]. [PERSON] et al.