Reliable downlink communication in satellite-to-underground networks remains challenging due to severe signal attenuation caused by underground soil and refraction in the air-soil interface. To address this, we propose a novel cooperative rate-splitting (CRS)-aided transmission framework, where an aboveground relay decodes and forwards the common stream to underground devices (UDs). Based on this framework, we formulate a max-min fairness optimization problem that jointly optimizes power allocation, message splitting, and time slot scheduling to maximize the minimum achievable rate across UDs. To solve this high-dimensional non-convex problem under uncertain channels, we develop a deep reinforcement learning solution framework based on the proximal policy optimization (PPO) algorithm that integrates distribution-aware action modeling and a multi-branch actor network. Simulation results under a realistic underground pipeline monitoring scenario demonstrate that the proposed approach achieves average max-min rate gains exceeding $167\%$ over conventional benchmark strategies across various numbers of UDs and underground conditions.

翻译：星地下行通信中，地下土壤引起的严重信号衰减以及空气-土壤界面的折射效应，使得高可靠通信仍面临挑战。为此，我们提出一种新型协作速率分裂（CRS）辅助传输框架，其中地面中继节点解码并向地下设备（UDs）转发公共流。基于该框架，我们构建了一个最大化最小公平性优化问题，通过联合优化功率分配、消息分裂与时隙调度，最大化所有UDs的最小可达速率。针对信道不确定条件下该高维非凸问题，我们提出基于近端策略优化（PPO）算法的深度强化学习求解框架，其整合了分布感知动作建模与多分支演员网络。在真实地下管道监测场景下的仿真结果表明，所提方法在不同UD数量及地下工况下，相较于传统基准策略可实现超过167%的平均最大最小速率增益。