  Satellite systems face a significant challenge in effectively utilizing
limited communication resources to meet the demands of ground network traffic,
characterized by asymmetrical spatial distribution and time-varying
characteristics. Moreover, the coverage range and signal transmission distance
of low Earth orbit (LEO) satellites are restricted by notable propagation
attenuation, molecular absorption, and space losses in sub-terahertz (THz)
frequencies. This paper introduces a novel approach to maximize LEO satellite
coverage by leveraging reconfigurable intelligent surfaces (RISs) within 6G
sub-THz networks. The optimization objectives encompass enhancing the
end-to-end data rate, optimizing satellite-remote user equipment (RUE)
associations, data packet routing within satellite constellations, RIS phase
shift, and ground base station (GBS) transmit power (i.e., active beamforming).
The formulated joint optimization problem poses significant challenges owing to
its time-varying environment, non-convex characteristics, and NP-hard
complexity. To address these challenges, we propose a block coordinate descent
(BCD) algorithm that integrates balanced K-means clustering, multi-agent
proximal policy optimization (MAPPO) deep reinforcement learning (DRL), and
whale optimization (WOA) techniques. The performance of the proposed approach
is demonstrated through comprehensive simulation results, exhibiting its
superiority over existing baseline methods in the literature.
