发表机构
TU Wien（维也纳科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种结合图神经网络与深度强化学习的可扩展框架，通过稀疏化信道图和局部化设计，在严格预算下将RIS分配覆盖概率提升约12%，并实现线性计算复杂度。
AI 中文摘要
可重构智能表面（RIS）为缓解6G多小区网络中的阻塞并扩展毫米波覆盖提供了一种有前景的范式。然而，在竞争的基站之间动态分配共享的RIS基础设施是一个NP难问题，带来了严重的可扩展性瓶颈。在本文中，我们提出了一种将图神经网络（GNN）与深度强化学习（DRL）相结合的可扩展框架，用于动态共享RIS编排。通过将分配问题表述为马尔可夫决策过程，我们引入了一种物理拓扑稀疏化策略，将稠密的信道矩阵剪枝为稀疏的三部图。这种剪枝将边密度降低了77%，并消除了表示噪声，从而在降低计算复杂度的同时提高了全局覆盖概率。我们的关系消息传递架构无需模型重新训练即可自然泛化到任意网络维度。此外，结构消融研究表明，物理路径损耗使表面依赖性局部化，从而实现了具有线性计算扩展性的高效局部图设计。在密集城市环境中的大量仿真表明，在严格的基础设施预算约束下，所提出的GNN-DRL框架在覆盖范围上始终优于贪婪启发式基线，最高提升约12%，同时通过GPU加速实现了更快的推理速度。
英文摘要
Reconfigurable Intelligent Surfaces (RISs) offer a promising paradigm to mitigate blockage and extend millimeter-wave coverage in 6G multi-cell networks. However, dynamically allocating shared RIS infrastructure across competing base stations is an NP-hard problem posing severe scalability bottlenecks. In this paper, we propose a scalable framework combining Graph Neural Networks (GNNs) with Deep Reinforcement Learning (DRL) for dynamic shared RIS orchestration. By formulating allocation as a Markov Decision Process, we introduce a physical topology sparsification strategy that prunes dense channel matrices into a sparse tripartite graph. This pruning reduces edge density by 77% and removes representation noise, thereby improving global coverage probability while reducing computational complexity. Our relational message-passing architecture naturally generalizes to arbitrary network dimensions without model retraining. Furthermore, structural ablation studies reveal that physical path loss localizes surface dependencies, enabling a highly efficient localized graph design with linear computational scaling. Extensive simulations in dense urban environments demonstrate that under strict infrastructure budget constraints, the proposed GNN-DRL framework consistently outperforms greedy heuristic baselines by up to ~12% in coverage while delivering faster inference speed via GPU acceleration.
