GRLR: Routing With Graph Neural Network and Reinforcement Learning for Mega LEO Satellite Constellations
Senbai Zhang, Aijun Liu, Chen Han, Xin Xu, Xiaohu Liang, Kang An, Yunyang Zhang
Abstract
This paper investigates the routing problem in the mega low earth orbit (mLEO) satellite constellations considering factors including distribution of the users, topology of the networks and dynamics of the inter-satellite links (ISLs). In walker-delta constellations, each satellite establishes four stable ISLs with neighboring satellites to forward data packets without relying on ground facilities. In order to minimize the delay from the source satellite to the destination satellite, the routing problem is formulated as a Markov decision problem (MDP) and the GRLR routing algorithm is proposed which integrates reinforcement learning (RL) and graph neural network (GNN) effectively. The GRLR establishes the decision network based on Actor-Critic RL framework and builds feature extraction network based on GNN to realize distributed intelligent routing decisions in mLEO constellations. Finally, the simulation experiments are carried out to illustrate that the proposed strategy exhibits rapid convergence and outperforms the baseline strategies in terms of delay and adaptability to network dynamics.