Dynamic Planning of Inter-Plane Inter-Satellite Links in LEO Satellite Networks

Abstract

Low Earth Orbit (LEO) satellite constellations are promising to provide global coverage and low latency communication by deploying a large number of small satellites and widely establishing Inter-Satellite Links (ISLs). However, due to the high motion of the LEO satellites, fixed inter-plane ISLs cannot provide long-time continuous connectivities and guarantee high-throughput communication performance. The existing dynamic planning approaches almost only consider part of the constellation information and cannot derive the optimal inter-plane ISLs. This paper proposes a dynamic Inter-plane Inter-satellite Links Planning method based on Multi-Agent deep reinforcement learning (MA-IILP) to optimize the total throughput and inter-plane ISL switching rate. We formulate a Partially Observable Markov Decision Process (POMDP) model with taking into account the Euclidean distance, communication rate and link switching cost. We derive the optimal strategy by utilizing Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm with a centralized training and distributed execution paradigm. Finally, extensive experiments are carried out and the results illustrate that our proposed approach can increase the total throughput of the target constellation by 2.8%∼7.2%, and decrease the inter-plane ISL switching rate by 30.7%∼68.4% compared to the state-of-the-art baseline algorithms.