AI 中文总结
本研究针对动态未知环境下RF场建模泛化难的问题，提出物理引导的RF世界模型RFWM，采用两阶段训练策略，在新构建的基准上实现了优于现有最优方法的RF场生成性能。
AI 中文摘要
射频（RF）辐射场建模对无线网络优化和感知至关重要，但在动态且未知的环境中仍具挑战性。现有基于学习的方法从稀疏测量值合成RF场，但多数难以泛化到动态且未知的环境。为解决此局限，我们提出RFWM，一种物理引导的RF世界模型，可将视觉动态、接入点（AP）配置等多模态物理条件映射到时域RF场。RFWM采用含物理引导先验和约束的两阶段训练策略：第一阶段，RFWM适配预训练的视觉扩散骨干网络至RF轨迹，从少量过去的RF输入预测RF序列，同时以Friis引导先验为骨干网络提供粗略衰减引导；第二阶段，RFWM通过从头训练ControlNet并微调适配RF的骨干网络学习物理到RF的映射，同时用六个物理引导正则化器强制执行细粒度传播一致性。跨高度头随后在一次前向传播中联合生成查询接收高度处的RF轨迹。我们构建了一个新基准，包含115个环境中平均每序列33帧的7715个序列，用于动态RF场生成。实验结果显示，在分布内和分布外设置下，RFWM的均方误差（MSE）相比现有最优方法分别降低约7dB和3dB。
英文摘要
Radio-frequency (RF) radiance-field modeling is essential for wireless network optimization and sensing, yet remains challenging in dynamic and unseen environments. Existing learning-based methods synthesize RF fields from sparse measurements, but most struggle to generalize to dynamic and unseen environments. To address this limitation, we propose RFWM, a physics-guided RF world model that maps multimodal physical conditions like visual dynamics and AP configurations to spatiotemporal RF fields. RFWM adopts a two-stage training strategy with physics-guided priors and constraints. In the first stage, RFWM adapts a pretrained visual diffusion backbone to RF trajectories to predict RF sequences from a few past RF inputs, while conditioning the backbone on a Friis-guided prior for coarse attenuation guidance. In the second stage, RFWM learns the physical-to-RF mapping by training a ControlNet from scratch and fine-tuning the RF-adapted backbone, while six physics-guided regularizers enforce fine-grained propagation consistency. Cross-height heads then jointly generate RF trajectories at queried receiver heights in one forward pass. We construct a new benchmark of 7,715 sequences averaging 33 frames across 115 environments for dynamic RF-field generation. Experimental results show that RFWM improves MSE by approximately 7 dB and 3 dB over the state of the art under in-distribution and out-of-distribution settings, respectively.
