Data-Driven Deployment of Reconfigurable Intelligent Surfaces in Cellular Networks

2025-10-11

AI Summary

Enhancing cellular coverage in urban environments remains challenging due to complex propagation conditions and infrastructure constraints. Method: This paper proposes the first fully automated, data-driven framework for joint optimization of reconfigurable intelligent surface (RIS) placement, orientation, configuration, and base station beamforming—compatible across 4G/5G/6G bands. It innovatively fuses real-world channel measurements with a physics-consistent ray-tracing model (built on Sionna), augmented by reflection/scattering-aware heuristic filtering, outage-user clustering, and multi-variable electromagnetic optimization to drastically reduce deployment complexity. Results: Experiments reveal that substantial coverage gains in dense urban settings require closely spaced, large-aperture RISs—yet diminishing returns impose cost-effectiveness bottlenecks. The framework is open-sourced, providing a reproducible methodology and benchmarking toolkit for scalable RIS deployment.

Technology Category

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

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

This paper presents a fully automated, data-driven framework for the large-scale deployment of reconfigurable intelligent surfaces (RISs) in cellular networks. Leveraging physically consistent ray tracing and empirical data from a commercial deployment in the UK, the proposed method jointly optimizes RIS placement, orientation, configuration, and base station beamforming in dense urban environments across frequency bands (corresponding to 4G, 5G, and a hypothetical 6G system). Candidate RIS locations are identified via reflection- and scattering-based heuristics using calibrated electromagnetic models within the Sionna Ray Tracing (RT) engine. Outage users are clustered to reduce deployment complexity, and the tradeoff between coverage gains and infrastructure cost is systematically evaluated. It is shown that achieving meaningful coverage improvement in urban areas requires a dense deployment of large-aperture RIS units, raising questions about cost-effectiveness. To facilitate reproducibility and future research, the complete simulation framework and RIS deployment algorithms are provided as open-source software.

Problem

• Automating RIS deployment in cellular networks using data-driven methods
• Jointly optimizing RIS placement and configuration with base stations
• Evaluating coverage-cost tradeoffs for dense urban network deployments

Innovation

• Data-driven framework automates large-scale RIS deployment
• Joint optimization of RIS placement and beamforming parameters
• Open-source simulation tools enable reproducible RIS research