Multi-Antenna Cooperative Scheduling Method for Satellite-To-Ground Link in LEO Satellite Networks

Abstract

In rapidly expanding low Earth orbit (LEO) satellite networks, the transmission capacity of satellite-to-ground downlinks has become a critical bottleneck for massive data backhaul. In practical systems, the severe supply-demand imbalance between limited gateway antennas and numerous passing satellites makes it impossible to establish links simultaneously, severely limiting the transmission capacity of the downlinks. To address this challenge, we propose a multi-antenna cooperative dynamic scheduling method. First, the antenna-satellite matching strategy is formulated as a mixed-integer nonlinear programming (MINLP) problem aimed at maximizing total system throughput. Second, given the NP-hard nature of this problem, it is further transformed into a Markov decision process (MDP), and a Cooperative Antenna Scheduling Reinforcement Learning (CAS-RL) algorithm is proposed. Finally, simulation results demonstrate that the proposed algorithm increases the average system throughput by more than $\mathbf{1 6}$% compared to benchmarks.