发表机构
KTH Royal Institute of Technology（瑞典皇家理工学院）
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URLLC的中断预测问题，提出物理信息VAE-EVT框架，在RadioMapSeer数据集上的0.1%分位数中断区域，SNR RMSE达4.83 dB，远优于现有GAN模型。
AI 中文摘要
超可靠低延迟通信（URLLC）需要精准识别信噪比（SNR）低于中断阈值的空间区域，此处的中断指SNR低于指定阈值，对于URLLC而言，该阈值可严格到SNR分布的0.1%分位数。传统生成式无线电地图模型往往聚焦于重构平均信号电平，常忽略对准确中断预测至关重要的低SNR。为解决这一局限，我们提出了物理与尾端感知的VAE-EVT（变分自编码器-极值理论）框架，该框架对SNR的主体分布和尾部分布分别建模。我们的方法始于一个物理信息预处理阶段，从场景几何中提取确定性特征，包括视距、阴影衰落和距离。随后，双潜在编码器采用高斯混合模型捕捉主体SNR，采用广义帕累托分布（GPD）捕捉尾部SNR。通过采用改进的变分目标，模型被训练为同时监督两种分布，确保关注极端衰落事件。在RadioMapSeer数据集上评估，我们的方法在由SNR的0.1%分位数低阈值定义的中断区域中，SNR的均方根误差（RMSE）为4.83 dB，显著优于基于生成对抗网络（GAN）的最先进模型，该模型的SNR RMSE为21.90 dB，且随着中断阈值变得更严格，性能差距会进一步扩大。
英文摘要
Ultra-reliable low-latency communication (URLLC) requires precise identification of spatial regions where the signal-to-noise ratio (SNR) falls below an outage threshold. In this context, an outage refers to instances in which SNR falls below a specified threshold, which, for URLLC, can be as stringent as the 0.1% quantile of the SNR distribution. Traditional generative radio map models tend to focus on reconstructing average signal levels, often overlooking the low SNR that is crucial for accurate outage prediction. To address this limitation, we introduce a physics- and tail-informed VAE-EVT (variational autoencoder-extreme value theory) framework that distinctly models both the bulk and tail distribution of SNR. Our approach begins with a physics-informed preprocessing stage that extracts deterministic features, including line-of-sight, shadowing, and distance, from the scene geometry. A dual-latent encoder then captures the bulk SNR using a Gaussian mixture and the tail using a generalized Pareto distribution (GPD). By employing a modified variational objective, the model is trained to jointly supervise both regimes, ensuring focused attention on extreme fading events. Evaluated on the RadioMapSeer dataset, our method achieves an SNR RMSE of 4.83 dB in the outage region defined by the low threshold of 0.1% SNR quantile. This significantly outperforms the state-of-the-art GAN-based model, which records an SNR RMSE of 21.90 dB, with the performance gap widening as the outage threshold becomes more stringent.
CommentsAccepted at the IEEE Global Communications Conference (GLOBECOM) 2026, Macau, China
