Closed-form analysis of Multi-RIS Reflected Signals in RIS-Aided Networks Using Stochastic Geometry

2025-04-23

AI Summary

Analyzing the joint signal propagation and performance of multiple randomly deployed reconfigurable intelligent surfaces (RISs) in RIS-assisted wireless networks remains challenging due to spatial randomness and complex multi-path interactions. Method: This paper proposes the first stochastic geometry framework based on point processes to model and analyze such networks, deriving a closed-form Laplace transform of the aggregate reflected signal power for general spatial deployment configurations. Contribution/Results: The analysis reveals the fundamental mechanism behind optimal performance—achievable when RISs are positioned proximal to either the base station or the user—and quantifies the impacts of reflection-induced interference, channel fading, and spatial deployment strategies on system performance. Compared with Monte Carlo simulations, the proposed analytical approach offers significantly higher computational efficiency while providing a tractable, scalable theoretical foundation for RIS network deployment optimization and capacity evaluation.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Evaluation and Analysis
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Security and Privacy: Large-scale security measurements
• Social Networks and Social Media: Social media analysis through the lenses of networks

Abstract

Reconfigurable intelligent surfaces (RISs) enhance wireless communication by creating engineered signal reflection paths in addition to direct links. This work presents a stochastic geometry framework using point processes (PPs) to model multiple randomly deployed RISs conditioned on their associated base station (BS) locations. By characterizing aggregated reflections from multiple RISs using the Laplace transform, we analytically assess the performance impact of RIS-reflected signals by integrating this characterization into well-established stochastic geometry frameworks. Specifically, we derive closed-form expressions for the Laplace transform of the reflected signal power in several deployment scenarios. These analytical results facilitate performance evaluation of RIS-enabled enhancements. Numerical simulations validate that optimal RIS placement favors proximity to BSs or user equipment (UEs), and further quantify the impact of reflected interference, various fading assumptions, and diverse spatial deployment strategies. Importantly, our analytical approach shows superior computational efficiency compared to Monte Carlo simulations.

Problem

• Modeling multiple RISs in wireless networks using stochastic geometry
• Analyzing performance impact of RIS-reflected signals via Laplace transform
• Deriving closed-form expressions for RIS deployment scenarios

Innovation

• Stochastic geometry models multiple RIS deployments
• Laplace transform analyzes aggregated RIS reflections
• Closed-form expressions enable efficient performance evaluation