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.

With the proliferation of smart devices and the emergence of high-bandwidth applications, Unmanned Aerial Vehicle (UAV)-assisted Device-to-Device (D2D) communications and Non-Orthogonal Multiple Access (NOMA) technologies are increasingly becoming important means of coping with the scarcity of the spectrum and with high data demand in future wireless networks. However, the efficient coordination of these techniques in complex and changing 3D environments still faces many challenges. To this end, this paper proposes a NOMA-based multi-UAV-assisted D2D communication model in which multiple UAVs are deployed in 3D space to act as airborne base stations to serve ground-based cellular users with D2D clusters. In order to maximize the system throughput, this study constructs an optimization problem of joint channel assignment, trajectory design, and power control, and on the basis of these points, this study proposes a joint dynamic hypergraph Multi-Agent Deep Q Network (DH-MDQN) algorithm. The dynamic hypergraph method is first used to construct dynamic simple edges and hyperedges and to transform them into directed graphs for efficient dynamic coloring to optimize the channel allocation process; subsequently, in terms of trajectory design and power control, the problem is modeled as a multi-agent Markov Decision Process (MDP), and the Multi-Agent Deep Q Network (MDQN) algorithm is used to collaboratively determine the trajectory design and power control of the UAVs. Simulation results show the following: (1) the proposed algorithm can achieve higher system throughput than several other benchmark algorithms with different numbers of D2D clusters, different D2D cluster communication spacing, and different UAV sizes; (2) the proposed algorithm designs UAV trajectory optimization with a 27% improvement in system throughput compared to the 2D trajectory; and (3) in the NOMA scenario, compared to the case of no decoding order constraints, the system throughput shows on average a 34% improvement.

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.

To address the critical issues of geometric ill-conditioning and non-line-of-sight (NLOS) interference faced by broadcast radio positioning systems in long-distance transmission (≥200 km) and low-altitude flight scenarios (1000 m to 3000 m), this paper proposes a Differential and Robust Positioning method for Airborne Platforms (DPAP). Integrating radio differential positioning, the proposed method enhances the single-point positioning algorithm through a grid search and iteratively reweighted least squares to mitigate geometric ill-conditioning and numerical instability in low-altitude environments. Furthermore, a passive differential positioning approach is introduced to eliminate common errors using neighboring reference stations. Finally, a scenario-aware safe fusion strategy ensures that the fused solution is never inferior to the optimal sub-solution under any circumstances. Simulation results demonstrate that, under conditions involving six ground stations, user-to-station distances of no less than 200 km, and 15% of links experiencing NLOS propagation, the differential and robust positioning method achieves a positioning accuracy of 0.588 m RMS. This represents an improvement of approximately one order of magnitude compared to RSPP (12.304 m), and outperforms traditional Huber M-estimation (0.678 m) and elevation-weighted least squares methods (1.462 m). All results are based on Monte Carlo simulations; real-world validation with SDR hardware and flight tests is left for future work. This work provides a scalable, infrastructure-light backup for safe UAV operations in GNSS-hostile environments, directly supporting the emerging low-altitude economy.