Deep Learning-Based Secure Scheduling and Cooperative Artificial Noise Generation in LEO Satellite Networks

As key enablers of 6G, low Earth orbit (LEO) satellite constellations are expected to offer global and low-latency connectivity. However, the broadcast nature and open-space propagation of satellite communications make it inherently vulnerable to eavesdropping. In dense LEO constellations where multiple satellites simultaneously cover overlapping regions, satellite scheduling can significantly contribute to secrecy performance. In this letter, we investigate a novel deep learning-based secure scheduling problem for physical layer security (PLS) in LEO satellite networks. During the scheduling process, we jointly consider multi-satellite transmission and cooperative artificial noise (AN) generation to defend against eavesdropping. The proposed deep learning-based scheduler, designed using attention-based permutation-invariant neural networks, learns security-oriented scheduling policies based solely on statistical channel information of eavesdroppers. Simulation results demonstrate that the proposed scheduling algorithm significantly enhances secrecy performance in terms of both secrecy rate and secrecy outage probability, outperforming conventional baseline schemes.