Towards Spatial Location Aided Fully-Distributed Dynamic Routing for LEO Satellite Networks

As the Low Earth Orbit (LEO) satellite has extremely high moving speed and limited networking resources, designing dynamic routing has become a promising approach to improve satellite communication performance. Due to the hundreds of satellites within a constellation and the complex attributes of each satellite, traditional routing strategies based on centralized paradigm derivation face increasingly complex challenges. To address these issues, this paper jointly optimizes queuing delay and propagation delay by proposing a fully distributed routing algorithm based on deep reinforcement learning. Each satellite builds a partially observable Markov decision process (POMDP) model based on the spatial location and queue length of surrounding nodes and adaptively selects the next hop by calculating the estimated residual propagation delay between the neighboring satellites and the destination satellite. Simulation analysis shows that our proposed method has tremendous advantages and effectiveness.