The reconfigurable intelligent surface (RIS) is a promising technology that is anticipated to enable high spectrum and energy efficiencies in future wireless communication networks. This paper investigates optimum location-based RIS selection policies in RIS-aided wireless networks to maximize the end-to-end signal-to-noise ratio for product-scaling and sum-scaling path-loss models where the received power scales with the product and sum of the transmitter-to-RIS and RIS-to-receiver distances, respectively. These scaling laws cover the important cases of end-to-end path-loss models in RIS-aided wireless systems. The random locations of all available RISs are modeled as a Poisson point process. To quantify the network performance, the outage probabilities and average rates attained by the proposed RIS selection policies are evaluated by deriving the distance distribution of the chosen RIS node as per the selection policies for both product-scaling and sum-scaling path-loss models. We also propose a limited-feedback RIS selection framework to achieve distributed network operation. The outage probabilities and average rates obtained by the limited-feedback RIS selection policies are derived for both path-loss models as well. The numerical results show notable performance gains obtained by the proposed RIS selection policies.

中文翻译：

---

无线网络的最佳可重构智能表面选择

可重构智能表面（RIS）是一种很有前途的技术，有望在未来的无线通信网络中实现高频谱和能源效率。本文研究了 RIS 辅助无线网络中基于位置的最佳 RIS 选择策略，以最大化产品缩放和和缩放路径损耗模型的端到端信噪比，其中接收功率与产品和发射器到 RIS 和 RIS 到接收器距离的总和，分别。这些缩放定律涵盖了 RIS 辅助无线系统中端到端路径损耗模型的重要案例。所有可用 RIS 的随机位置都被建模为泊松点过程。为了量化网络性能，建议的 RIS 选择策略获得的中断概率和平均速率通过根据产品缩放和总和缩放路径损耗模型的选择策略推导所选 RIS 节点的距离分布来评估。我们还提出了一个有限反馈的 RIS 选择框架来实现分布式网络操作。通过有限反馈 RIS 选择策略获得的中断概率和平均速率也适用于两种路径损耗模型。