Hierarchical Resource Optimization for Covert SAGINs: A Stackelberg-Matching Game Approach

Abstract

To strengthen the security of large-scale space-air-ground integrated networks (SAGINs), this paper investigates covert communication against non-cooperative ground base stations (BSs) that detect satellite transmissions via signal power monitoring. In this scenario, numerous low-earth-orbit (LEO) satellites deployed across multiple orbital layers provide backhaul support for autonomous aerial vehicles (AAVs), thereby serving ground users. To reduce the probability of detection, the LEO network performs resource allocation to conceal transmission activities under co-channel interference. However, such a strategy may overlook fairness in resource optimization, potentially undermining cooperation between LEO satellites and terrestrial networks. To address this issue, we develop a two-stage hierarchical Stackelberg matching game to characterize the interaction between LEO satellites and non-cooperative ground BSs. At the upper stage, the LEO network acts as the leader and maximizes the communication rate through power allocation while satisfying covert constraints. At the lower stage, the non-cooperative ground BSs act as followers and competitively minimize their detection errors in response to the leader’s actions. To solve this problem efficiently, we integrate hierarchical game theory, the asynchronous Stackelberg decision transformer (ASDT), and multi-agent reinforcement learning (MARL) into a unified framework for multi-agent coordination. Numerical results demonstrate the effectiveness of the proposed hierarchical resource optimization strategy and provide useful insights for secure SAGIN deployment.