## 1 Introduction  
Sixth-generation (6G) wireless networks are expected to provide large-scale connectivity for millions of interconnected devices, providing different quality-of-service (QoS) requirements, ubiquitous coverage, high embedded artificial intelligence (AI), efficient energy utilization, and adaptive network security [1]. Advances in manufacturing technology have led to a reduction in the cost of individual unmanned aerial vehicles (UAVs) while enhancing their mobility, deployment efficiency, and flexibility [2, 3]. UAVs should play an important role in the 6G ecosystem, as flight equipment is expected to occupy dense airspace for transportation, monitoring, network relay, and other tasks [4]. UAVs communicate with other UAVs and satellite stations, forming a space-air network, which can become a part of 6G heterogeneous networks. The deployment of large-scale UAVs integrated with highly reliable networks and advanced intelligent collaboration algorithms has become a prominent research focus in recent years. A UAV swarm composed of large-scale small and low-cost drones is expected to achieve higher and greater performance relative to independent drones through collaboration and information exchange. In many applications, such as surveillance, reconnaissance, and search and rescue, UAV swarms need to constantly exchange data with each other and with rear command posts during the execution of tasks, to improve task performance, survivability, and scalability. Especially in intensive, dynamic, and time-critical tasks, the need for wide bandwidth, high capacity, high date rates, and low latency is increasing [5]. A distributed network structure is more suitable for large-scale UAV swarm networks. This is because the distributed network has better anti-destruction and self-healing capabilities, and does not stop working due to the destruction of a single node [6]. A heterogeneous network includes a UAV swarm ad hoc network, and the satellite network is a typical application method in offshore and remote areas. However, UAV swarms have many challenges when resource management is implemented in such networks. First, the UAV nodes of the swarm are densely distributed, and interference among the UAV swarm seriously affects the performance of the ad hoc network. Second, the network topology may change rapidly and dynamically due to the high mobility of the drones, resulting in frequent switching and mutual interference of the network connections. Third, in addition to mutual interference within the UAV swarm that may cause a complex electromagnetic environment, the existence of malicious interference also causes complications. A UAV swarm requires the constant interaction of location and status information for swarm control algorithms such as path planning and collision avoidance. These algorithms require high frequency and accuracy of messages. Collision and packet loss may significantly reduce the coordination algorithm performance of UAV swarms. Thus, addressing the internal interference of UAVs in a dynamic-topology environment has become the core key problem of large-scale UAV swarm ad hoc networks. UAVs are increasingly being used in communication networks, with typical applications such as IoT data acquisition and 6G flying base stations. In these applications, UAV path planning is an important topic. The communication supported by unmanned aerial vehicles (UAVs) has become a highly attractive technology in the field of wireless sensor networks (WSNs) for efficient data collection [7; 8; 9]. Collecting data from ground-distributed sensor network is one of the key technologies of WSNs. The typical goals of trajectory optimization are to minimize the task time of unmanned aerial vehicles [10; 11], minimize energy consumption during flight [12; 13], maximize the amount of data collected [14], and maximize the number of service sensors [15]. UAV-assisted 5G/6G networks can improve network efficiency and flexibility by providing seamless on-demand ultra-high-capacity connections for elastic traffic in these areas.