Topology Generation of UAV Covert Communication Networks: A Graph Diffusion Approach with Incentive Mechanism

2025-08-08

AI Summary

To address the challenges of dynamic mobility and covert communication exposure in unmanned aerial vehicle (UAV) networks deployed for sensitive urban applications—such as surveillance and emergency response—this paper proposes a self-organizing topology generation framework integrating generative AI with game-theoretic incentives. Methodologically, it introduces Graph Diffusion Policy Optimization (GDPO) into UAV topology control for the first time, jointly leveraging a Stackelberg game incentive mechanism to co-optimize network connectivity and node cooperation willingness under sparsity constraints. Key contributions include: (1) establishing the first generative-AI-driven paradigm for covert topology synthesis; (2) achieving joint optimization of covertness, robustness, and cooperativeness; and (3) demonstrating through experiments that the framework significantly improves topology convergence speed, connectivity quality, and low-probability-of-intercept (LPI) communication performance—enhancing robustness by 32.7% over baseline approaches.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Game Theory and Economic Paradigms: Cooperative Game Theory
• Search and Optimization: Learning to Search

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

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.

Problem

• Ensuring reliable covert communication in dynamic UAV networks
• Balancing connectivity and sparsity in UAV network topologies
• Incentivizing cooperative relay behaviors among self-interested UAVs

Innovation

• Graph Diffusion-based Policy Optimization for dynamic topologies
• Stackelberg Game-based incentive mechanism for cooperation
• Generative AI for sparse, well-connected network generation