Deep Reinforcement Learning-Based QoE-Driven Multi-UAV 3D Adaptive Deployment Strategy and Secure Consensus Control

In this paper, we propose a quality of experience (QoE) driven multi-UAV networks deployment strategy based on deep reinforcement learning (DRL) and secure consensus protocols. UAVs can adjust their moving direction and distance in three-dimensional (3D) space to serve users who move randomly in the target area, which ensures that all the users are able to get the communication service while making the total communication service quality maximized. Additionally, the proposed method accounts for UAV speed variations and endogenous safey. Through the implementation of multivariate constraints and secure consensus protocols, all normal nodes maintain a robust topology, effectively resisting interference from malicious nodes, ensuring speed remains within a safe range, and ultimately achieving state consensus. Simulation results show that the method can quickly converge to the optimal state with fewer iterations, outperform the traditional method in terms of QoE performance, and ensures the flight safety of UAV formations in the case of attacks by malicious nodes.