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.
翻译:本研究提出了一种完全自动化的可重构智能表面部署策略,并通过基于Sionna射线追踪技术构建的英国城市数字孪生系统进行了验证。在经实测数据校准的场景中,该方法联合优化了可重构智能表面的位置、朝向、配置以及基站波束成形,覆盖4G、5G及假设的6G频段。候选部署站点通过基于散射的射线识别,同时采用用户聚类技术降低部署开销。结果表明,要实现有效的覆盖增强需要密集部署大孔径可重构智能表面,这引发了对大规模可重构智能表面应用的实际可行性与成本的质疑。