最优多智能反射面放置：非均匀用户分布下保证覆盖的和速率最大化
Optimal Multi-RIS Placement: Coverage-Guaranteed Sum Rate Maximization Under Inhomogeneous User Distributions
- Purdue University（普渡大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
研究在非均匀用户分布下，通过解决约束集覆盖问题、贪心最小划分及贝叶斯优化方法，实现以最少RIS数量保证概率覆盖并最大化系统预期和速率，联合达成良好覆盖与吞吐量。
AI中文摘要：
无线系统中可重构智能表面（RIS）的全部潜力实现与其战略空间部署相关。现有文献主要通过最大化覆盖来实现公平以穿越障碍物，但往往无法利用用户密度的空间分布来最大化吞吐量。为此，我们提出一个新的分层问题，在以最少数量的RIS保证概率覆盖的同时最大化系统的预期和速率。通过在可见性图上解决约束集覆盖问题获得可部署RIS以提供覆盖保证的最优区域，通过在由最优区域形成的相交超图上进行贪心最小划分得到满足覆盖保证所需的最小RIS数量，最后用基于贝叶斯优化的方法计算最终的最优RIS放置。数值结果表明该框架能在无不切实际系统假设的情况下实现良好的覆盖和吞吐量。
英文摘要：
Reconfigurable Intelligent Surface (RIS) has emerged as a promising next-generation technology that improves the throughput and coverage of a wireless system. The realization of the full potential of RISs in a wireless system is tied to their strategic spatial deployment. While existing literature on RIS placement primarily focuses on maximizing coverage, when multiple RIS placements guarantee the required coverage (happens quite often), these approaches fail to exploit prior user trends to choose the one that is most probable to maximize throughput. Thus, to enable throughput maximization while guaranteeing fairness, we formulate a novel hierarchical problem that maximizes the expected sum rate of the system while guaranteeing a certain probabilistic coverage, with the requisite minimum number of RISs deployed. To solve this multi-layered non-convex problem, firstly, we obtain a set of optimal points where we can deploy RISs to provide the coverage guarantee. Then, the least number of RISs that can guarantee the required coverage is obtained by a greedy minimum partitioning. Finally, a Bayesian optimization based approach is used to compute the optimal RIS placement. Numerical results are provided to show that the proposed framework consistently identifies placements that jointly achieve good coverage and throughput, without impractical assumptions.
