Interference Propagation Analysis for Large-Scale Multi-RIS-Empowered Wireless Communications:An Epidemiological Perspective

Reconfigurable intelligent surfaces (RISs) have gained significant attention in recent years due to their ability to control the reflection of radio-frequency signals and reshape the wireless propagation environment. Unlike traditional studies that primarily focus on the advantages of RISs, this paper examines the negative impacts of RISs by investigating interference propagation caused by user mobility in downlink wireless systems. We employ a stochastic geometric model to simulate the locations of base stations and RISs using the Matérn hard core point process, while user locations are modeled with the homogeneous Poisson point process. We derive novel closed-form expressions for the power distributions of the received signal at the users and the interfering signal. Additionally, we present a novel expression for coverage probability and introduce the concept of interference propagation intensity. To characterize the dynamics of interference caused by user mobility, we adopt an epidemiological approach using the susceptible-infected-susceptible model. Finally, crucial factors influencing the propagation of interference are analyzed. Numerical results validate our theoretical analysis and provide suggestions for managing interference propagation in large-scale multi-RIS wireless communication networks.

💡 Research Summary

This paper investigates the often‑overlooked downside of deploying large numbers of reconfigurable intelligent surfaces (RISs) in downlink cellular networks: the propagation of interference caused by user mobility. While most RIS literature focuses on signal enhancement and coverage gains, the authors treat RIS‑induced reflections from non‑serving base stations (BSs) as a source of additional interference, and they model the dynamic spread of this interference using an epidemiological susceptible‑infected‑susceptible (SIS) framework.

System Modeling

• Spatial distribution: Base stations and RISs are modeled by a Matérn hard‑core point process (MHCPP) to enforce a minimum inter‑site distance, reflecting realistic deployment constraints. User equipment (UE) locations follow a homogeneous Poisson point process (HPPP).
• Channel model: Direct BS‑UE links experience Rayleigh fading; RIS‑reflected links follow Nakagami‑m fading. Path loss follows a product‑distance model: (PL_{ij k}=C(d_{ij}d_{jk})^{-\alpha}). Each RIS consists of (N) passive elements with 2‑bit phase resolution, enabling near‑optimal coherent beamforming.
• Signal model: The received signal at a typical UE (U_0) includes the direct component and the RIS‑reflected component from its serving BS. Interference originates from all other BS‑RIS pairs (Eq. 5). SINR is defined in Eq. 7.

Analytical Development

• The authors approximate the sum of desired and interfering powers by Gamma distributions (gamma‑approximation), which yields closed‑form PDFs, CDFs, and ultimately a tractable expression for coverage probability as a function of the SINR threshold (T).
• A new metric, interference propagation intensity (\beta), is introduced to quantify how strongly interference spreads through the network.
• User mobility is captured by a random‑walk model (direction uniformly distributed in (