Data-driven Analysis of the Cost-Performance Trade-off of Reconfigurable Intelligent Surfaces in a Production Network.

This paper presents a comprehensive study on the deployment of Reconfigurable Intelligent Surfaces (RIS) in urban environments with poor radio coverage. We focus on the city of London, a large metropolis where radio network planning presents unique challenges due to diverse geographical and structural features. Using crowd-sourced datasets, we analyze the Reference Signal Received Power (RSRP) from end-user devices to understand the existing radio coverage landscape of a major Mobile Network Operator (MNO). Our study identifies areas with poor coverage and proposes the deployment of RIS to enhance signal strength and coverage. We selected a set of potential sites for RIS deployment and, combining data from the MNO, data extracted from a real RIS prototype, and a ray-tracing tool, we analyzed the gains of this novel technology with respect to deploying more conventional technologies in terms of RSRP, coverage, and cost-efficiency. To the best of our knowledge, this is the first data-driven analysis of the cost-efficiency of RIS technology in the production of urban networks. Our findings provide compelling evidence about the potential of RIS as a cost-efficient solution for enhancing radio coverage in complex urban mobile networks. More specifically, our results indicate that large-scale RIS technology, when applied in real-world urban mobile network scenarios, can achieve 72% of the coverage gains attainable by deploying additional cells with only 22% of their Total Cost of Ownership (TCO) over a 5-year timespan. Consequently, RIS technology offers around 3x higher cost-efficiency than other more conventional coverage-enhancing technologies.