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.

翻译：本文提出了一种全自动、数据驱动的框架，用于在蜂窝网络中大规模部署可重构智能表面（RIS）。该方法利用物理一致的射线追踪技术和来自英国商业部署的经验数据，在密集城市环境中，针对多个频段（对应4G、5G及一个假设的6G系统），联合优化RIS的放置位置、朝向、配置以及基站波束成形。候选RIS位置通过基于反射和散射的启发式方法识别，该方法在Sionna射线追踪（RT）引擎中使用经过校准的电磁模型。中断用户被聚类以降低部署复杂度，并且系统地评估了覆盖增益与基础设施成本之间的权衡。研究表明，要在城市区域实现有意义的覆盖改善，需要密集部署大口径RIS单元，这引发了关于成本效益的疑问。为了促进可复现性和未来研究，完整的仿真框架和RIS部署算法已作为开源软件提供。