Localization in outdoor wireless systems typically requires transmitting specific reference signals to estimate distance (trilateration methods) or angle (triangulation methods). These cause overhead on communication, need a LoS link to work well, and require multiple base stations, often imposing synchronization or specific hardware requirements. Fingerprinting has none of these drawbacks, but building its database requires high human effort to collect real-world measurements. For a long time, this issue limited the size of databases and thus their performance. This work proposes significantly reducing human effort in building fingerprinting databases by populating them with \textit{digital twin RF maps}. These RF maps are built from ray-tracing simulations on a digital replica of the environment across several frequency bands and beamforming configurations. Online user fingerprints are then matched against this spatial database. The approach was evaluated with practical simulations using realistic propagation models and user measurements. Our experiments show sub-meter localization errors on a NLoS location 95\% of the time using sensible user measurement report sizes. Results highlight the promising potential of the proposed digital twin approach for ubiquitous wide-area 6G localization.

翻译：户外无线系统中的定位通常需要传输特定参考信号以估计距离（三边测量法）或角度（三角测量法）。这些方法会引入通信开销、依赖视距链路实现良好性能，且需要多个基站，往往对同步或硬件有特殊要求。指纹定位虽无上述缺陷，但其数据库构建需要耗费大量人力进行实测数据采集。长期以来，这一问题限制了数据库规模及其性能。本文提出通过填充数字孪生射频地图来显著降低指纹数据库构建中的人力成本。这些射频地图基于环境数字副本，结合多个频段与波束赋形配置的射线追踪仿真生成。在线用户指纹随后与该空间数据库进行匹配。该方法采用实际传播模型与用户测量数据的仿真实验进行评估。实验表明，在合理用户测量报告规模下，95%的非视距定位误差可控制在亚米级。研究结果揭示了所提出的数字孪生方法在实现6G泛在广域定位中的巨大潜力。