Reconfigurable intelligent surfaces (RISs) provide alternative routes for reflected signals to network users, offering numerous applications. This paper explores an innovative approach of strategically deploying RISs along road areas to leverage various propagation and blockage conditions present in cellular networks with roads. To address the local network geometries shown by such networks, we use a stochastic geometry framework, specifically the Cox point processes, to model the locations of RISs and vehicle users. Then, we define the coverage probability as the chance that either a base station or an RIS is in line of sight (LOS) of the typical user and that the LOS signal has a signal-to-noise ratio (SNR) greater than a threshold. We derive the coverage probability as a function of key parameters such as RIS density and path loss exponent. We observe that the network geometry highly affects the coverage and that the proposed RIS deployment effectively leverages the underlying difference of attenuation and blockage, significantly increasing the coverage of vehicle users in the network. With experimental results addressing the impact of key variables to network performance, this work serves as a versatile tool for designing, analyzing, and optimizing RIS-assisted cellular networks with many vehicles.

翻译：可重构智能表面（RIS）为反射信号提供了到达网络用户的替代路径，具有广泛的应用前景。本文探讨了一种创新方法：沿道路区域战略性部署RIS，以利用道路环境中蜂窝网络存在的多样化传播与遮挡条件。为刻画此类网络呈现的局部几何特征，我们采用随机几何框架（具体而言是Cox点过程）对RIS与车辆用户的位置进行建模。随后，我们将覆盖概率定义为典型用户与基站或RIS之间存在视距（LOS）链路，且该视距链路的信噪比（SNR）超过特定阈值的概率。我们推导出覆盖概率作为RIS密度、路径损耗指数等关键参数的函数表达式。研究发现，网络几何结构对覆盖性能具有显著影响，而所提出的RIS部署方案能有效利用衰减与遮挡的底层差异性，显著提升网络中车辆用户的覆盖范围。通过实验验证关键变量对网络性能的影响，本研究成果为多车辆场景下RIS辅助蜂窝网络的设计、分析与优化提供了通用分析工具。