## 1 Introduction  
The increasing popularity of the internet and the widespread adoption of IoT devices have led to a surge in data traffic, putting enormous pressure on traditional network architectures [1]. Edge caching, a distributed storage architecture, offers a potential solution by storing data closer to the end users, reducing data transfer delays and network congestion. In recent years, unmanned aerial vehicles (UAVs) have attracted considerable attention due to their maneuverability and efficiency. UAVs outfitted with advanced transceivers and batteries have seen extensive application in wireless communication systems [2]. Unlike conventional communication systems, UAVs establish wireless links with users via line-of-sight technology, which can markedly enhance the transmission rate [3]. Therefore, in areas with inadequate network infrastructure or severe network congestion, UAVs can be used as a complementary platform for edge caching, facilitating the timely and efficient storage and transmission of data to network users. Due to the flexibility of UAVs, UAV-assisted edge cache systems have been paid more attention. In the UAV-assisted wireless communication network scenario, UAVs as edge cache devices providing communication services for users and the corresponding wireless resource allocation problems have been studied. In [4], the authors studied the enhancement of system performance through user-centric caching UAV network cooperation in a cache-enabled UAV network. The article proposes a strategy for optimizing the probabilistic cache distribution to minimize the system outage probability, and takes into account the dynamic changes in the interference topology of UAVs. The work in [5] tackled the concurrent optimization of caching and resource allocation in cache-enabled UAV networks. A distributed algorithm based on a machine learning framework was used to predict the content request distribution, achieving optimization of cache space allocation. In [6], a Q-learning-based algorithm was proposed for joint cache placement, UAV trajectory design, and resource allocation in dynamic cache-enabled UAV network scenarios. The work in [7] introduces an online algorithm, OOA, for user preference-oriented service caching and task offloading in UAV-assisted mobile edge computing networks. The algorithm aims to minimize overall service delay by dynamically making caching decisions based on user equipment service preferences and UAV historical trajectories. OOA employs a greedy approach to enhance caching effectiveness and reduce overhead, maximizing the probability of successful offloading. The work in [8] focuses on multi-UAV assisted wireless networks, aiming to jointly optimize cache placement, UAV trajectories, and transmission power within a limited timeframe to enhance the minimum throughput for UAVs serving users. In [9], the authors consider a mobile edge computing network where multiple unmanned aerial vehicles with caching and computing capabilities are deployed. The weighted sum of the content cache hit rate and the service delay contraction rate is defined as the average quality of experience (QoE) of the network. To address the problem of maximizing the average QoE and the practical constraints of UAV caching and computing capabilities, algorithms based on Gibbs sampling and matching are proposed to solve the problem in an iterative manner. Considering the frequent application of mobile terminals and the increase in chip energy consumption, it has become increasingly challenging for mobile user terminals to meet the growing service quality requirements due to their limited power. In order to address this issue in the context of mobile edge cache scenarios, wireless power transfer (WPT) technology is being utilized within the network to provide energy transmission services for mobile user terminals [10]. UAVs offer high-mobility efficiency and can bypass terrain obstacles, making them suitable as mobile charging devices for providing terminal endurance needs within the network.