Stochastic Geometry Analysis of RIS-Assisted Cellular Networks with Reflective Intelligent Surfaces on Roads

TL;DR

The paper tackles large-scale connectivity in RIS-assisted cellular networks by strategically placing RISs along roads and modeling RISs, vehicle users, and BSs with a joint Cox/PPP stochastic geometry framework. It derives a closed-form LOS-based coverage probability that captures how road-specific geometry, RIS density, and RIS element count ($N_r^2$) interact with urban path-loss differences ($\alpha_1$ vs $\alpha_2$) and blockage parameters ($\eta_1$, $\eta_2$). The key contributions include (i) a roadside deployment model that yields explicit coverage expressions as functions of $\mu$, $\lambda$, $\nu$, $\lambda_2$, $N_r^2$, and path-loss exponents, (ii) a theoretical demonstration that vehicle users gain more from roadside RISs than handset users under the same network, and (iii) practical design insights showing how RIS density, element count, and traffic patterns jointly influence coverage, enabling deployment decisions without extensive simulations. The findings emphasize that exploiting road-area propagation and blockage characteristics via roadside RISs can substantially boost large-scale connectivity, guiding future 5G/6G deployments with vehicle-centric requirements, while acknowledging ideal RIS operation and no-interference assumptions as design benchmarks.

Abstract

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.

Figures (15)

• Figure 1: In this paper, only RIS-to-vehicle links are on roads and they are referred to as linear links having path loss exponent $\alpha_1$ and blockage parameter $\eta_1$. All other links are planar links having path loss exponent $\alpha_2$ and blockage parameter $\eta_2.$
• Figure 2: Base stations and handset users are Poisson point processes of densities $3/\text{km}^2$ and $100/\text{km}^2$, respectively. RISs and vehicles users are Cox point processes with densities $\lambda_l\mu=10/\text{km}^2$ and $\lambda_l\nu = 100/\text{km}^2,$ respectively.
• Figure 3: Base stations and handset users are Poisson point processes of density $3/\text{km}^2$ and $100/\text{km}^2$, respectively. RISs and vehicles users are Cox point processes of dentieis $\lambda_l\mu=6/\text{km}^2$ and $\lambda_l\nu = 100/\text{km}^2,$ respectively.
• Figure 4: The coverage probability of the typical user without RISs. We use $\gamma=83$ dB.
• Figure 5: The coverage probability with no RIS. $\gamma=83$ dB and $\gamma=97$ dB.

Theorems & Definitions (9)

• Remark 1
• Example 1
• Remark 2
• Remark 3
• Remark 4
• Lemma 1
• Theorem 1
• Remark 5
• Example 2