Inter-Satellite Link Re-Planning Algorithm under Link Failures of LEO Satellite Constellations

The Low Earth Orbit (LEO) satellite constellation has been recognized as an important component of the future 6G network. However, due to the high-speed movement of LEO satellites and the potential for link failures, achieving optimal satellite communication performance with a static inter-satellite links (ISLs) scheme is challenging. To solve this problem, this paper proposes an ISL re-planning algorithm with considering link failures based on multi-agent deep reinforcement learning (named ReISL). In ReISL, a multi-objective optimization problem is formulated to maximize the system capacity while minimizing the link switching costs. Then, multi-agent deep reinforcement learning is employed to derive the optimal ISL re-planning schemes, where each satellite utilizes Double Deep Q-Network (DDQN). Finally, extensive experiments are carried out and the results demonstrate that our proposed algorithm ReISL can outperform the baseline algorithms.