## 1 Introduction
This is crucial because trajectory planning determines the overall travel time, the services provided to the GU, and the energy consumed by the drone [16]. The optimization problem of drone trajectory planning is similar to the well-known Traveling Salesman Problem (TSP) [15; 16; 17] and Vehicle Routing Problem (VRP) [18]. A simple method that ensures collision-free transmission and guarantees communication latency between UAVs is to use the time division multiple access (TDMA) protocol. The fixed TDMA protocol can avoid conflicts, but the time slot cannot be flexibly scheduled, which is inefficient in large-scale UAV swarm networks. To solve this problem, some TDMA-based protocols for vehicle ad hoc networks have been proposed [19; 20; 21; 22]. These protocols eliminated many collisions by dividing different driving direction nodes into different time slot sets. However, these protocols of slot partitioning in VANETs were not suited for UAV swarm networks, because the movement direction of UAVs was arbitrary. In [23], a collision-free MAC protocol, termed CF-MAC, was introduced. This protocol enabled UAVs equipped with half-duplex radios and omnidirectional antennas to swiftly access networks while employing a region-marking scheme that diminished the collision probability to nearly zero. In [24], a token-based protocol was proposed, which used full-duplex radios and multipacket reception capability to achieve a higher throughput and a lower delay. A concurrent transmission MAC protocol that works by combining millimeter, directional antenna and TDMA for UAV ad hoc networks was proposed to meet the requirements of UAV ad hoc networks in terms of bandwidth, data rate and delay [25]. These protocols compromised on either conflict or flexible scheduling, or needed the addition of full-duplex, directional antennas, millimeter waves, or even shared location information, and were not widely applied due to their complexity. Game theory is a branch of applied mathematics that is a natural and powerful tool for analyzing the interactions between multiple decision-makers. It is mainly used to study competition or cooperative behavior in economics [26]. The resource management problem for large-scale UAV communication networks, from a game-theoretic perspective, was investigated in [6], and it was exactly aligned with the distributed and autonomous nature of such networks. A network-side self-organized coalition formation approach for intra-tier interference mitigation, primarily through neighborhood cooperation, was proposed in [27]; by forming disjoint coalitions, small-cell access points (SAPs) were able to mitigate the interference within a coalition and thus improved their achievable data rates. A distributed coalition formation algorithm for cooperation in ad hoc networks was proposed in [28], which used the joining and leaving rules for the choice of each node to assure that the newly formed coalitional structure could converge to a stable structure. The coalition formation game proposed in the above study could coordinate internal interference in the network by aggregating adjacent nodes to form an independent coalition, but they reduced only the degree of internal interference and could not avoid interference among nodes. To solve the challenges of resource management and routing protocols, a cross-layer optimization method was presented with a novel mean field game (MFG) in [29], which could select the optimal data transmission path through the management and allocation of frequency resources and power resources. The algorithm of forming coalitions required full channel status information and the results of each iteration, which was difficult to obtain in ad hoc networks. Ref. [27] obtained CSIs and iteration results through a central base station, while [28] did not explain where these were obtained. These methods typically form coalitions among UAVs that are in close spatial proximity to reduce internal network interference, thereby achieving greater throughput.