A Topology Design Method for Satellite Networks based on Deep Reinforcement Learning

Recently, Low Earth Orbit (LEO) satellite constellations with low-latency and high-bandwidth attract extensive research. However, most available studies focused on the field of satellite network routing algorithms, ignoring the impact of topology on the efficiency of inter-satellite networking and the quality of inter-satellite communication. In this paper, we propose a satellite network topology design method based on deep reinforcement learning (DRL), with the goal of reducing the latency of the entire satellite network. To achieve this goal, we first model the satellite network communication scene and formulate the topology optimization problem as a Markov decision process (MDP). Then, we further propose the idea of backbone-point satellites and use DRL to optimize the topology structure. Finally, we conduct extensive experiments on different performances of satellite topology, and we conclude that the network topology constructed in this way can provide lower latency communications than the motif and +Grid topologies, optimized by 8.48% and 42.86% respectively.