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.

翻译：卫星系统在有效利用有限通信资源以满足地面网络流量需求方面面临重大挑战，这些流量具有非对称空间分布和时变特性。此外，低地球轨道（LEO）卫星的覆盖范围和信号传输距离受到亚太赫兹（THz）频段中显著传播衰减、分子吸收和空间损耗的限制。本文提出了一种新颖方法，通过在6G亚太赫兹网络中利用可重构智能超表面（RIS）实现LEO卫星覆盖最大化。优化目标包括提升端到端数据速率、优化卫星与远程用户设备（RUE）的关联、卫星星座内的数据包路由、RIS相位偏移以及地面基站（GBS）发射功率（即主动波束成形）。所构建的联合优化问题因其时变环境、非凸特性和NP-hard复杂度而面临显著挑战。为解决这些问题，我们提出了一种块坐标下降（BCD）算法，该算法集成了平衡K均值聚类、多智能体近端策略优化（MAPPO）深度强化学习（DRL）和鲸鱼优化（WOA）技术。通过全面的仿真结果展示了所提方法的性能，并证明其优于文献中现有的基线方法。