The multi-UAV relay system can rapidly deploy a temporary communication network in disaster emergency communication scenarios to enhance communication coverage and stability in the affected area and ensure the efficient transmission of rescue information. Aiming at the problems of insufficient real-time performance, low user fairness, and low utilization of communication resources in multi-UAV relay systems, this paper proposes a joint optimization method of communication rate based on Lexicographic Optimization (LO). The multi-UAV relay scenario using multi-carrier non-orthogonal multiple access (MC-NOMA) technology is modeled as a multi-objective optimization problem and solved in stages using the LO method. Specifically, the first stage maximizes the minimum user communication rate by jointly optimizing the user association, relay UAV transmit power and flight trajectory; the second stage optimizes the modulation order of the relay UAVs to maximize the communication rate between UAV base stations. Finally, the proposed algorithm is compared under different optimization schemes and different optimization algorithms. The simulation results show that the user communication rate of the proposed algorithm is \(6.8\times {10}^5bps/s\) higher than that of the static user association scheme, \(6.7\times {10}^6bps/s\) higher than that of the static UAV trajectory scheme, and \(2.0\times {10}^5bps/s\) higher than that of the traditional Jara algorithm, and \(6.7\times {10}^6bps/s\) higher than that of the K-mean based optimization algorithm. Moreover, this algorithm performs well in convergence and stability, effectively improves the user communication rate, the communication rate between UAV base stations, and the energy utilization of relay UAV, and enhances the adaptability of the system to the dynamic environment.

To address the issues of communication failure and inefficiency in clustered drone relay communication due to external malicious interference, this paper proposes a joint optimization method for relay communication rates under interference conditions for clustered drones. This method employs the following two-step processing framework: Firstly, the Discrete Soft Actor-Critic (DSAC) algorithm is used to train the relay drones for dynamic channel selection, effectively avoiding various types of interference. Simultaneously, the Bayesian optimization algorithm is applied to optimize the hyperparameters of the DSAC algorithm, further enhancing its performance. Subsequently, the modulation order, transmission power, trajectory of the relay drones, and power allocation factors of the clustered drones are jointly optimized. This complex problem is transformed into a convex subproblem for determining a solution, aiming to maximize the communication rate of the clustered drones. The simulation’s results demonstrate that the proposed algorithm exhibits excellent performances in terms of anti-interference capability, solution convergence, and stability. It effectively improves the mission efficiency of clustered drones under interference conditions and enhances their adaptability to dynamic environments.