Data-Driven Deployment of Reconfigurable Intelligent Surfaces in Cellular Networks

TL;DR

The paper addresses the challenge of deploying reconfigurable intelligent surfaces (RIS) in dense urban cellular networks, where joint optimization of RIS placement, orientation, phase configuration, and base-station beamforming is essential. It introduces a data-driven framework that couples site-specific ray tracing (via Sionna RT) with two RIS-location strategies—reflection-based and scattering-based—alongside outage clustering and automated re-configuration. By calibrating material properties with real measurement data and evaluating across 4G, 5G, and a hypothetical 6G band, the work quantifies the substantial RIS density and aperture required to achieve meaningful coverage gains, highlighting a significant cost-benefit challenge for outdoor urban deployment. The results suggest that, despite its potential in niche scenarios, RIS may not be cost-effective for broad outdoor coverage in dense cities, but the open-source framework provides a reproducible platform for ongoing RIS research and benchmarking.

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.

Figures (11)

• Figure 1: 3D visualization of the area under consideration, produced with OpenStreetMap. Black pins indicate BS sites.
• Figure 2: RSRP heatmaps across the considered urban area for 4G, 5G, and 6G.
• Figure 3: RSRP distribution before RIS deployment for 4G (blue), 5G (orange), and 6G (green).
• Figure 4: Number of clusters formed by the BIRCH algorithm vs. parameter $T$ across the considered frequency bands.
• Figure 5: Scatter plot of outage UEs and their clustering.