Site-Specific RIS Deployment in Cellular Networks via Calibrated Ray Tracing

2025-10-10

AI Summary

This work addresses the feasibility and cost bottlenecks of deploying Reconfigurable Intelligent Surfaces (RIS) in urban cellular networks. Methodologically, it introduces a fully automated joint optimization framework grounded in calibrated ray-tracing digital twins, integrating Sionna-based ray tracing, empirical channel measurement calibration, electromagnetic RIS modeling, and multi-band (4G/5G/6G) beamforming. Candidate RIS locations are identified via scattering-ray analysis, while user clustering reduces deployment scale. Its key contributions include the first cross-generation frequency-band co-optimization and quantitative assessment of large-scale RIS deployment necessity. Results demonstrate that substantial coverage gains require high-density, large-aperture RIS configurations—highlighting fundamental feasibility and economic viability challenges in practical deployment. The framework establishes a scalable, empirically verifiable digital twin paradigm for RIS network planning.

Technology Category

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

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web applications in cross-disciplinary domains and verticals such as mixed reality, smart cities, and digital health
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

This work introduces a fully-automated RIS deployment strategy validated through a digital twin, powered by Sionna ray tracing, of a UK city. On a scene calibrated with measured data, the method jointly optimizes RIS placement, orientation, configuration, and BS beamforming across 4G, 5G, and hypothetical 6G frequencies. Candidate RIS sites are identified via scattering-based rays, while user clustering reduces deployment overhead. Results show that meaningful coverage enhancement requires dense, large-aperture RIS deployments, raising questions about the practicality and cost of large-scale RIS adoption.

Problem

• Optimizing RIS placement and configuration for cellular networks
• Validating deployment strategy through calibrated ray tracing digital twin
• Assessing coverage enhancement feasibility with dense RIS deployments

Innovation

• Automated RIS deployment strategy using digital twin
• Joint optimization of RIS placement and beamforming across frequencies
• Candidate site identification via scattering-based rays clustering