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The Low Earth Orbit (LEO) satellite constellation has the advantages of low-latency transmission and global coverage and has a wide range of applications in emergency rescue, aviation, and sea voyages. With the highly dynamic topologies, limited onboard computing resources, and large global routing computation overheads of the large-scale LEO constellation, centralized routing algorithms always encounter difficulties in such LEO satellite networks. Therefore, this paper proposes a flexible feedback intelligent routing algorithm based on deep Q-network (DQN) for large-scale LEO networks. By dynamically designing the reward function of DQN and adding a congestion alleviation scheme to the K-shortest path method in the training process, the delay and packet loss performances are improved. The delay performance is improved by 8%-12% over the Shortest Path First (SPF) algorithm, which is closer to the Shortest Delay First (SDF) algorithm with a minimum difference of only 6%. At the same time, the proposed algorithm has better packet loss performances than SPF and SDF algorithms to avoid congestion. When the congestion occurs, the algorithm can choose other paths.
Luo et al. (Mon,) studied this question.