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

SpaceRIS: LEO Satellite Coverage Maximization in 6G Sub-THz Networks by MAPPO DRL and Whale Optimization