# Joint Optimization of Relay Communication Rates in Clustered Drones under Interference Conditions
## 1 Introduction
Reference [8] proposes a convergent rate-partitioning-based interference management resource allocation and clustering algorithm for iteratively optimizing drone transmission power, drone pairing, wireless resource scheduling, and wireless resource pricing to achieve interference management in cellular drone networks. Reference [9] proposes a drone-path-tracking scheme that uses a radial basis neural network to approximate the adaptive approximation law of the gyroscopic effect function to balance the effects of system uncertainties and nonlinearities, improving the convergence speed and error accuracy of the controller. Reference [10] designed a joint optimization model that provides optimal truck and drone routing policies to address mission abortion when truck and clustered drone routes are subjected to random attacks and to minimize the total cost. Reference [11] designed a dynamic-based communication antijamming decision-making method to solve the problem of intelligent antijamming decision making for battlefield communication, enabling the intelligent system to effectively avoid jamming while ensuring uninterrupted communication as much as possible. Current research on antijamming primarily concentrates on methods to eliminate and mitigate the impacts of interference on communication processes. However, this focus often overlooks the crucial aspect of enhancing the system performance of clustered drones post-elimination of interference, which is essential for bolstering their mission execution capabilities. Consequently, the joint optimization of the \"residual performance\" of clustered drones following antijamming efforts holds significant research value, promising substantial improvements in their operational effectiveness. Reference [12] jointly optimizes the trajectory and scheduling plan of each drone, combining convex optimization and ant colony optimization algorithms to obtain the proposed optimal solution, maximizing the total amount of relay data. Reference [13] proposes a drone relay vehicle networking architecture based on relay protocols with nonorthogonal multiple access and maximum ratio combination techniques, as well as improved particle swarm optimization algorithms to achieve increased data rates for cell-edge vehicles in rural road scenarios. Reference [14] investigates the problem of clustered drone deployment for better coverage and proposes a genetic algorithm that encodes the solution of the problem as a chromosome and simulatesthe process of biological evolution to find favorable solutions, balancing between the energy consumption of all drones and the maximization of the lifetime of the full coverage network. Reference [15] proposes a 3D layout analysis framework for coordinating a fleet of drone relays and uses it to construct a complex mixed-integer nonconvex planning problem for network performance optimization in terms of user throughput fairness through a parallel alpha-fair drone deployment method. Reference [16] considers a scenario in which flying drones provide wireless services to multiple ground nodes simultaneously and proposes an effective joint transmission power and trajectory optimization algorithm that can maximize the minimum average throughput for a given length of time. Based on the considerations outlined above, this paper delves deeper into the subject, and the contributions are as follows:  
* Aiming to maximize the communication rate among clustered drones, this study constructs a multivariable coupled 0-1 mixed nonlinear integer programming model.