A Maximum Trusted Distance-Based Greedy Q-Learning Routing Strategy for LEO Satellite Networks

Large-scale low Earth orbit (LEO) satellite networks are characterized by massive node populations, rapid topology changes, and resource-constrained individual satellites. Existing routing technologies in such networks face challenges such as slow convergence, excessive end-to-end hop counts, and prolonged latency. To address these issues in applying Qlearning to large-scale LEO constellation routing, this study proposes a Maximum Trusted Distance-Based Greedy QLearning Routing (MTD-GQR) strategy. The approach first optimizes Q-tables through a greedy mechanism to accelerate convergence. It then extends beyond traditional topological constraints, where satellites link only with four adjacent neighbors, by leveraging maximum trusted distance to define inter-satellite connectivity. Simulations comparing the proposed strategy with the Dijkstra algorithm and recent Q-learning-based approaches for large-scale constellations demonstrate significant improvements: a 26.7% reduction in convergence time, a 15.8% decrease in average end-to-end hop count, and a 3.1% reduction in average latency. These results highlight the effectiveness of MTD-GQR in enhancing routing performance for large-scale LEO satellite networks.