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

TL;DR

This work addresses interference propagation in downlink networks powered by large-scale multi-RIS deployments under user mobility. It builds a stochastic-geometry framework with MHCPP-distributed BSs/RISs and PPP-distributed UEs, derives closed-form power-distribution results, and uses a gamma-approximation to obtain a new coverage probability expression. A novel SIS-based epidemic model is introduced to quantify infection and recovery rates, yielding an interference-propagation intensity metric that captures phase-transition behavior (R0>1 vs R0≤1). Numerical results validate the analytical framework and reveal how RIS size, element count, and node densities shape propagation, offering practical guidance for RIS deployment in interference-dominated environments.

Abstract

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.

Figures (10)

• Figure 1: The structure of the paper.
• Figure 2: The considered multi-RIS-assisted multi-cell downlink wireless system. A typical user $U_0$ serves as the observation target in our interference propagation analysis. Each BS owns a dedicated RIS 10670007, enabling both direct and RIS-reflected transmission paths. Solid lines represent the desired signal paths received by $U_0$, including both direct BS-UE links and RIS-reflected BS-RIS-UE links. Blue dashed lines denote interference originating from interfering BSs and their associated RISs. Red dashed lines represent additional interference caused by the movement of other users, whose reflected signals via nearby RISs unintentionally contaminate $U_0$'s reception, thereby propagating interference dynamically.
• Figure 3: Cumulative distribution function of the desired signal.
• Figure 4: Outage probability with/without UE movement.
• Figure 5: Interference propagation under different initial states and interference conditions: low ((a) and (d) subfigures); medium ((b) and (e) subfigures); and high ((c) and (f) subfigures). The initial number of susceptible UEs and the number of infected UEs are $95$ and $5$, respectively, in the top three subfigures, whereas their respective numbers in the bottom three subfigures are $50$ and $50$.

Theorems & Definitions (7)

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• Remark 1
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• Remark 2
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• Remark 4