This paper considers the joint TN-NTN constrained resource allocation, where terrestrial base stations and non-terrestrial base stations coexist in the spectrum. We focus on large-scale and practical scenarios characterized by large numbers of transmission channels and users, alongside highly dynamic user behaviors. As common learning solutions fail to address these challenges, we propose a decomposition solution based on the special properties of the cross-segment interference, and then tackle the original problem via solving subproblems in a sequential learning manner. Furthermore, to enhance the flexibility of the learned policies, we design a stochastic training environment that captures the key characteristics of real-world systems. Simulation results tested on the full 20MHz bandwidth with various numerologies show that our solution significantly improves scalability compared to existing solutions and remains robust in highly dynamic scenarios.

翻译：本文研究了天地一体化网络中受约束的联合资源分配问题，其中地面基站与非地面基站在频谱中协同共存。我们聚焦于具有大规模传输信道与用户数量、以及高度动态用户行为的实际应用场景。由于传统学习方法难以应对这些挑战，我们基于跨区段干扰的特殊性质提出了一种分解解决方案，进而通过顺序学习的方式求解子问题来处理原始优化问题。此外，为增强学习策略的灵活性，我们设计了一个能捕捉真实系统关键特征的随机训练环境。在完整20MHz带宽及多种参数配置下的仿真结果表明，相较于现有方案，我们的解决方案显著提升了系统可扩展性，并在高度动态场景中保持稳健性能。