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

TL;DR

This paper introduces a novel framework combining graph diffusion and incentive mechanisms to dynamically generate covert UAV communication networks that adapt to changing environments and promote cooperation.

Contribution

It presents a new self-organizing UAV network model using AI-driven topology generation and game-theoretic incentives for covert communication.

Findings

Effective topology generation with high connectivity

Improved covert communication performance

Model converges reliably in dynamic scenarios

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.