Multi-Agent-Driven Cognitive Secure Communications in Satellite-Terrestrial Networks

• URL: http://arxiv.org/abs/2602.06048v1
• Date: Tue, 06 Jan 2026 10:30:41 GMT
• Title: Multi-Agent-Driven Cognitive Secure Communications in Satellite-Terrestrial Networks
• Authors: Yujie Ling, Zan Li, Lei Guan, Zheng Zhang, Shengyu Zhang, Tony Q. S. Quek,
• Abstract summary: Malicious eavesdroppers pose a serious threat to private information via satellite-terrestrial networks (STNs)<n>We propose a cognitive secure communication framework driven by multiple agents that coordinates spectrum scheduling and protection through real-time sensing.<n>We exploit generative adversarial networks to produce adversarial matrices, and employ learning-aided power control to set real and adversarial signal powers for protection layer.
• Score: 58.70163955407538
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Satellite-terrestrial networks (STNs) have emerged as a promising architecture for providing seamless wireless coverage and connectivity for multiple users. However, potential malicious eavesdroppers pose a serious threat to the private information via STNs due to their non-cooperative behavior and ability to launch intelligent attacks. To address this challenge, we propose a cognitive secure communication framework driven by multiple agents that coordinates spectrum scheduling and protection through real-time sensing, thereby disrupting the judgment of eavesdroppers while preserving reliable data transmission. On this basis, we formulate an optimization problem to maximize the secrecy probability of legitimate users, subject to a reliable transmission probability threshold. To tackle this problem, we propose a two-layer coordinated defense system. First, we develop a foundation layer based on multi-agent coordination schedule to determine the satellite operation matrix and the frequency slot occupation matrices, aiming to mitigate spectrum congestion and enhance transmission reliability. Then, we exploit generative adversarial networks to produce adversarial matrices, and employ learning-aided power control to set real and adversarial signal powers for protection layer, which actively degrades the inference capability of eavesdroppers. Simulation results demonstrate that the proposed method outperforms benchmark methods in terms of enhancing security performance and reducing power overhead for STNs in the cognitive secure communication scenario.

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