发表机构
Terahertz Wireless Communications (TWC) Interdisciplinary Research Center, Shanghai Jiao Tong University; Huawei Technologies Company Ltd.; School of Information Science and Electronic Engineering, Shanghai Jiao Tong University（上海交通大学太赫兹无线通信（TWC）跨学科研究中心; 华为技术有限公司; 上海交通大学信息科学与工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对太赫兹无线数据中心，提出基于三频段信道测量的多层数字孪生框架，构建感知视距的AI信道孪生体，实现更低功率重构误差与超90%覆盖范围，支撑高效规划优化。
AI 中文摘要
AI计算的快速增长推动了对灵活且高容量的数据中心互连日益增长的需求。太赫兹（THz）通信凭借其超宽带宽和高空间复用能力，已成为未来无线数据中心的有前景解决方案，而数字孪生（DTs）可实现高效的无线规划和实时优化。在本研究中，提出了一种用于太赫兹无线数据中心的测量驱动型多层DT框架，其中从底层到顶层逐步构建物理层、信道层、评估层和操作层。首先，在140GHz、220GHz和300GHz下开展了广泛的信道测量，以表征与频率相关的传播行为。基于三频段测量，通过联合优化几何结构、材料、天线和混合传播模型，建立了经测量校准的物理孪生体。在物理孪生体之上，开发了一种感知视距（LoS）的隐式神经场，以构建用于高效信道重构的AI信道孪生体。所提出的AI孪生体从校准后的孪生体中学习与位置相关的信道统计信息，实现接收功率和LoS概率的实时预测。在重构的信道场基础上，推导了系统级评估层，用于分析接入点（AP）到机架以及机架到机架通信的覆盖范围和干扰。实验结果表明，与现有的神经场基线相比，所提出的AI孪生体实现了更低的功率重构误差，同时保持了实时推理能力。此外，采用天花板安装的AP部署方式，在10dB的信干噪比（SINR）阈值下实现了超过90%的覆盖范围，证明了所提出的DT框架在太赫兹无线数据中心规划和优化中的有效性。
英文摘要
The rapid growth of AI computing has driven increasing demands for flexible and high-capacity data-center interconnections. Owing to its ultra-wide bandwidth and high spatial reuse capability, terahertz (THz) communication has emerged as a promising solution for future wireless data centers, while digital twins (DTs) enable efficient wireless planning and real-time optimization. In this work, a measurement-driven multi-layer DT framework is proposed for THz wireless data centers, where the physical, channel, evaluation, and manipulation layers are progressively constructed from bottom to top. First, extensive channel measurements are conducted at 140, 220, and 300 GHz to characterize frequency-dependent propagation behaviors. Based on the tri-band measurements, a measurement-calibrated physical twin is established by jointly optimizing the geometry, material, antenna, and hybrid propagation models. On top of the physical twin, a line-of-sight (LoS)-aware implicit neural field is developed to construct an AI channel twin for efficient channel reconstruction. The proposed AI twin learns location-dependent channel statistics from the calibrated twin, enabling real-time prediction of received power and LoS probability. Building upon the reconstructed channel field, a system-level evaluation layer is derived to analyze coverage and interference for both AP-to-rack and rack-to-rack communications. Experimental results show that the proposed AI twin achieves lower power reconstruction error than existing neural-field baselines while maintaining real-time inference capability. Moreover, the ceiling-mounted AP deployment achieves over 90% coverage under a 10 dB signal-to-interference-plus-noise ratio (SINR) threshold, demonstrating the effectiveness of the proposed DT framework for THz wireless data-center planning and optimization.
