Combating AI-Based Jamming in LEO Satellite Networks Using Quantum Adversarial Deep Reinforcement Learning

In recent years, the demand for seamless connectivity and highly efficient, reliable network services for low earth orbit (LEO) satellites has escalated. To meet these expectations, a critical issue that must be addressed is combating malicious jamming attacks on satellite networks, which occur due to the open nature of satellite-ground connections. Moreover, in the era of artificial intelligence (AI), AI-based jamming poses a severe threat to the security of satellite networks and disrupts secure communications, particularly given the dynamic movements of LEO satellites and the time-sequential complexity of such attacks. Accordingly, this paper proposes a quantum adversarial deep reinforcement learning (QADRL) approach to mitigate AI-based jamming attacks while enhancing the quality-of-service (QoS) for LEO satellite networks. Specifically, the proposed QADRL approach is based on a zero-sum Markov game utilizing two opposing learning networks: one optimizing satellite routing links to avoid jamming and improve QoS, while the other, focuses on the jammer, optimizes the trajectory, jamming nodes, and power of unmanned aerial vehicles (UAVs) to maximize jamming success. The results demonstrate that the proposed QADRL outperforms classical adversarial DRL (CADRL) by reducing the jamming success rate by 33.33% and increasing the average QoS of the satellite network by 18.4975%.