Terahertz (THz) wireless communication has emerged as a promising solution for future data center interconnects; however, accurate channel characterization and system-level performance evaluation in complex indoor environments remain challenging. In this work, a measurement-calibrated AI-assisted digital twin (DT) framework is developed for THz wireless data centers by tightly integrating channel measurements, ray-tracing (RT), and implicit neural field (INF) modeling. Specifically, channel measurements are first conducted using a vector network analyzer at 300 GHz under both line-of-sight (LoS) and non-line-of-sight (NLoS) scenarios. RT simulations performed on the Sionna platform capture the dominant multipath structures and show good consistency with measured results. Building upon measurement and RT data, an RT-conditioned INF is developed to construct a continuous radio-frequency (RF) field representation, enabling accurate prediction in RT-missing NLoS regions. The comprehensive RF map generated by DT can provide system-level analysis and decisions for wireless data centers.

翻译：太赫兹（THz）无线通信已成为未来数据中心互联的一种有前景的解决方案，然而在复杂室内环境中进行精确的信道表征和系统级性能评估仍具挑战性。本文通过紧密集成信道测量、射线追踪（RT）和隐式神经场（INF）建模，开发了一种测量校准的AI辅助太赫兹无线数据中心数字孪生（DT）框架。具体而言，首先使用矢量网络分析仪在300 GHz频段下，针对视距（LoS）与非视距（NLoS）两种场景进行信道测量。基于Sionna平台进行的射线追踪仿真捕获了主要多径结构，并与测量结果表现出良好的一致性。基于测量与射线追踪数据，开发了射线追踪条件化的隐式神经场（RT-conditioned INF），用于构建连续的射频（RF）场表示，从而在射线追踪缺失的非视距区域实现精确预测。该数字孪生生成的综合射频图可为无线数据中心提供系统级分析与决策支持。