AI 中文总结
该研究针对W波段工业OFDM链路的数字孪生信道保真度问题，提出SC-SLA校准框架，通过对齐多维度信道统计量实现仿真到实验室的适配，在95 GHz及92-94 GHz频段均取得优于基线的性能。
AI 中文摘要
数字孪生（DT）可降低工业无线网络的空中验证成本，但其效用取决于底层信道孪生体（CT）的保真度。在W波段，特定站点的射线追踪能捕获确定性传播几何，但90 GHz以上实验室正交频分复用（OFDM）测量中观测到的小尺度损伤及捕获间变异性，无法在其信道频率响应（CFR）中复现。本文提出统计一致性仿真到实验室适配（SC-SLA）框架，用于将95 GHz Sionna射线追踪CT的保真度向测试床对齐，对齐指标包括平均功率延迟轮廓（PDP）、均方根时延扩展（τ_rms）的分布，以及50 MHz采样带宽下的每个子载波统计量。SC-SLA采用受生成对抗网络（GAN）启发的循环一致性架构，生成器为ResNet，损失函数为PDP、子带PDP、τ_rms矩与分位数，以及平均CFR幅度轮廓的归一化均方误差（NMSE）等批量级信道统计损失；该框架为非对抗性，无需配对的仿真/实测样本，也无需判别器。在保留的95 GHz数据上，相较于损伤增强的射线追踪输入，SC-SLA将τ_rms的柯尔莫哥洛夫-斯米尔诺夫（KS）统计量从0.86降至0.050；相较于四种监督基线（FCNN、CNN1D、BiLSTM、UNet1D）中性能最强者，该统计量降低了44%（从0.089降至0.050）。无需重新训练，同一检查点即可泛化到92-94 GHz载波，在所有基线中实现最低的PDP和CFR幅度误差，同时减少与未校准孪生体相比的τ_rms分布失配。
英文摘要
Digital twins (DTs) can reduce over-the-air validation cost in industrial wireless networks, but their utility depends on the fidelity of the underlying channel twin (CT). At W-band, site-specific ray tracing captures deterministic propagation geometry, yet its channel frequency responses (CFRs) do not reproduce the small-scale impairments and capture-to-capture variability observed in laboratory orthogonal frequency-division multiplexing (OFDM) measurements above 90 GHz. This paper proposes Statistics-Consistent Sim-to-Lab Adaptation (SC-SLA), a calibration framework that improves the fidelity of a 95 GHz Sionna ray-traced CT toward that of the testbed by aligning the mean power delay profile (PDP), the distribution of root-mean-square delay spread ($τ_{\mathrm{rms}}$), and per-subcarrier statistics at 50 MHz sampling bandwidth. SC-SLA uses a generative adversarial network (GAN)-inspired, cycle-consistent architecture with ResNet generators and batch-level channel-statistics losses on the PDP, sub-band PDP, $τ_{\mathrm{rms}}$ moments and quantiles, and normalized mean-square error (NMSE) of the mean CFR-magnitude profile. The framework is non-adversarial and requires neither paired simulated/measured samples nor discriminators. On held-out 95 GHz data, SC-SLA reduces the $τ_{\mathrm{rms}}$ Kolmogorov-Smirnov (KS) statistic from 0.86 to 0.050 relative to the impairment-augmented ray-traced input, and by 44% (from 0.089 to 0.050) relative to the strongest of four supervised baselines (FCNN, CNN1D, BiLSTM, and UNet1D). Without retraining, the same checkpoint also generalizes to 92-94 GHz carriers, where it achieves the lowest PDP and CFR-magnitude errors among all baselines while reducing the $τ_{\mathrm{rms}}$-distribution mismatch relative to the uncalibrated twin.
CommentsSubmitted for possible publication to IEEE. Paper currently under review. The contents of this paper may change at any time without notice
