TRACE：从ISAC测量中学习无线数字孪生的自校准
TRACE: Learning to Self-Calibrate Wireless Digital Twins from ISAC Measurements
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中文总结 AI 辅助
提出TRACE框架，利用无线网络自身射频测量与数字孪生渲染间的残差对齐，学习校正孪生中的建筑物位置和朝向误差，显著提升三维定位精度，并验证了跨场景泛化能力。
中文摘要 AI 辅助
无线数字孪生（DT）依赖三维环境模型来预测无线电传播并支持无线网络决策，然而这些模型通常从不完善的三维地图初始化。建筑物位置、高度、占地面积和朝向的误差因此可能导致高保真传播引擎模拟出错误的物理环境。在本文中，我们研究已部署的无线网络如何利用自身的射频（RF）测量来修复现有的数字孪生。具体而言，我们提出了孪生残差对齐与校准引擎（TRACE），这是一种基于物理的、学习驱动的自校准框架，将孪生维护视为物理世界与当前数字孪生之间的残差对齐。TRACE使用与物理测量相同的传感配置，对当前数字孪生进行射线追踪，将测量和模拟的射频相干反投影到共同的全局网格上，并提取每个建筑物当前数字孪生位置周围的相同局部区域。随后，一个多视角校正器融合来自传感节点和邻近建筑物的证据，预测每个建筑物的门控六参数校正，不依赖绝对布局或传感器顺序，并通过重新渲染支持迭代校正。在来自未见模拟场景的5,400个保留样本上（28 GHz），TRACE将三维位置均方根误差（RMSE）从2.202米降低到0.302米，偏航角RMSE从4.978°降低到0.894°，在布局、建筑物数量、传感节点数量和信噪比（SNR）变化下优于ViT和U-Net基线。在来自NIST室外庭院的实测28 GHz射频数据上，仅用合成射频训练的模型将平均平面墙体位置误差从1.00米降低到7.8厘米，无需实测数据微调或几何标签。这些结果表明，测量射频与孪生渲染射频之间的差异可以作为修复无线数字孪生的学习信号。
英文摘要
Wireless digital twins (DTs) rely on 3D environment models to predict radio propagation and support wireless-network decisions, yet these models are often initialized from imperfect 3D maps. Errors in building position, height, footprint, and orientation can therefore cause a high-fidelity propagation engine to simulate the wrong physical environment. In this paper, we study how a deployed wireless network can repair an existing DT using its own radio frequency (RF) measurements. In particular, we introduce Twin Residual Alignment and Calibration Engine (TRACE), a physics-grounded learning-based self-calibration framework that treats twin maintenance as residual alignment between the physical world and the current DT. Using the same sensing configuration as the physical measurements, TRACE ray-traces the current DT, coherently backprojects the measured and simulated RF onto a common world grid, and extracts the same local region around each building's current DT position. A multi-view corrector then fuses evidence across sensing nodes and neighboring buildings to predict a gated six-parameter correction per building, without relying on absolute layout or sensor ordering, and supports iterative correction through re-rendering. On 5,400 held-out samples from unseen simulated scenes at 28 GHz, TRACE reduces 3D position RMSE from 2.202 m to 0.302 m and yaw RMSE from 4.978° to 0.894°, outperforming ViT and U-Net baselines under changes in layout, building count, sensing-node count, and SNR. On measured 28 GHz RF data from the NIST outdoor courtyard, a model trained only on synthetic RF reduces mean planar wall-position error from 1.00 m to 7.8 cm, without measured-data fine-tuning or geometric labels. These results show that the discrepancy between measured and twin-rendered RF can serve as a learning signal for repairing a wireless DT.
