This invention discloses a
reinforcement learning-based method for optimizing the performance of FAS-assisted
satellite-to-ground communication. In air-to-ground communication, when the
direct path is blocked by obstacles, the
signal is transmitted through a mixture of reflection from moving scatterers and the
direct path. This invention describes the communication environment between the mobile
transmitter and the ground user through geometric stochastic modeling, establishes a dynamic optimization framework based on FAS, couples
satellite motion characteristics with adjustable FAS parameters to construct a three-dimensional channel model, designs an improved Memetic PPO
reinforcement learning algorithm to dynamically optimize the FAS configuration set at the ground user in the discrete action space through a local search mechanism, and jointly optimizes
channel capacity and modeling accuracy to generate the optimal strategy. This method reveals the nonlinear relationship between
satellite motion and channel characteristics, significantly improves communication performance in complex obstruction environments, provides a new method for
dynamic channel optimization of satellites, and has significant application value.