# Joint Optimization of Relay Communication Rates in Clustered Drones under Interference Conditions
## 1 Introduction  
In recent years, with the advancements in multi-agent technology, efficient computing technology, network convergence technology, and new communication technologies, various types of equipment have shed the \"fight alone\" approach. Instead, they operate in clusters, networking and collaborating to execute various tasks, thereby effectively enhancing the system's ability to handle complexity, dynamics, and adverse conditions. However, in this process, effectively managing system resources, ensuring fair efficiency, scheduling tasks, and combating external interference have become new challenges in meeting application requirements. These challenges are currently hotspots in both academia and industry. As an important application product of multi-agent systems, clustered drones have a broad application space [1]. They form an unmanned network system that is low cost with high dynamics and strong stability due to resource integration, self-organized collaboration, and intelligent scheduling. This provides strong support for the future construction of air, sky, earth, and sea globalized networks, the realization of communication-perception-computing integration, and the development and construction of unmanned platforms. Reference [2] introduces the use of multiple drones to form an integrated IoT between sky and Earth, redeploying multiple drones to a different location, changing the resource allocation, and offloading server computations by alternating heuristic greedy and successive convex approximation methods to minimize the total computational overheadof ground-based IoT devices. Reference [3] proposes a generalized intelligent collaborative task scheduling framework that switches between heuristic and deep reinforcement learning-based scheduling solutions to address the ever-changing task requests when dynamically served by multiple drones. Reference [4] applies multiple drones as airborne relays to serve clustered users on the ground and proposes a channel statistical model based on the probability of occurrence. It also establishes analytic expressions for the average rate and the average outage probability of this channel model, comparing its performance with those of the Rayleigh channel and Rice channel models. Reference [5] proposes a traffic load balancing scheme for a multi-drone-assisted Fog network to minimize wireless delays for IoT users. Reference [6] proposes a framework that utilizes a dynamic drone network for the heuristic optimization of coverage areas. This framework employs Bezier curves to plan the flight paths of drones, enabling intelligent adjustments to their positions and trajectories, which significantly enhances the network access quality for ground users. Reference [7] conducted an in-depth investigation into routing protocols based on clustered drone networks, comparing the characteristics and performances of various protocols to provide researchers with a basis for selecting appropriate ones. Despite the promising results demonstrated by the solutions proposed in the studies referenced, there is a notable lack of consideration for real-world environments and complex scenarios, which introduces certain limitations. Specifically, in the presence of malicious dynamic interference and a complex electromagnetic environment, the communication processes of clustered drones are inevitably impacted. Under these conditions, the signal-to-noise ratios of the received signals are affected by more than just the drones' trajectories and transmission powers. Therefore, it is essential to conduct a thorough analysis of the physical impact of the interference on the entire communication process and to develop tailored algorithms designed to mitigate such external interference.