Topology Generation of UAV Covert Communication Networks: A Graph Diffusion Approach with Incentive Mechanism
This addresses the challenge of secure and adaptive UAV networks for sensitive applications like urban monitoring and emergency response, representing an incremental improvement through hybrid methods.
This paper tackled the problem of ensuring reliable connectivity and covert communication in dynamic UAV networks by proposing a self-organizing framework combining Graph Diffusion-based Policy Optimization and a Stackelberg Game-based incentive mechanism, resulting in enhanced covert communication performance validated through extensive experiments.
With the growing demand for Uncrewed Aerial Vehicle (UAV) networks in sensitive applications, such as urban monitoring, emergency response, and secure sensing, ensuring reliable connectivity and covert communication has become increasingly vital. However, dynamic mobility and exposure risks pose significant challenges. To tackle these challenges, this paper proposes a self-organizing UAV network framework combining Graph Diffusion-based Policy Optimization (GDPO) with a Stackelberg Game (SG)-based incentive mechanism. The GDPO method uses generative AI to dynamically generate sparse but well-connected topologies, enabling flexible adaptation to changing node distributions and Ground User (GU) demands. Meanwhile, the Stackelberg Game (SG)-based incentive mechanism guides self-interested UAVs to choose relay behaviors and neighbor links that support cooperation and enhance covert communication. Extensive experiments are conducted to validate the effectiveness of the proposed framework in terms of model convergence, topology generation quality, and enhancement of covert communication performance.