WiNeRF：用于可操作无线信道建模的测量约束辐射场
WiNeRF: Measurement Constrained Radiance Fields for Actionable Wireless Channel Modeling
- University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
- Intel（英特尔）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
WiNeRF提出一种测量约束的神经辐射场框架，从稀疏CSI中学习连续复值信道表示，在NLoS室内环境实现5.3 dB中位预测SNR，比基线高4.9 dB，并支持波束成形等任务。
中文摘要 AI 辅助
无线嵌入式系统日益依赖无线信道信息进行决策，然而实际平台在严苛约束下运行，包括天线数量少、带宽窄以及稀疏且有噪声的测量。虽然受神经辐射场（NeRFs）启发的基于神经场的方法近来已被探索用于连续无线信道建模，但现有方法依赖密集测量或外部先验（如已知几何、视觉上下文或到达角（AoA）信息），限制了其在实际部署中的实用性。我们提出WiNeRF，一种神经场框架，直接从商用WiFi设备收集的稀疏信道状态信息（CSI）中学习空间连续、复值的无线信道表示。WiNeRF通过3D锥形波采样模型、多分辨率隐式场景表示以及用于复值信道学习的可微分优化框架，将内在系统约束（如天线几何、有限空间分辨率和相位不确定性）作为归纳偏置嵌入。在具有非视距（NLoS）区域的多种室内环境中，WiNeRF实现了5.3 dB的中位预测信噪比，平均比先前的神经基线高出4.9 dB（预测信噪比约提高3倍），并产生一种任务无关的信道表示，可直接复用于标准信号处理流程，包括波束成形、AoA估计和RSSI覆盖映射，而无需修改现有硬件或无线协议。
英文摘要
Wireless embedded systems increasingly rely on wireless channel information for decision making, yet practical platforms operate under severe constraints, including few antennas, narrow bandwidth, and sparse, noisy measurements. While neural field based approaches inspired by Neural Radiance Fields (NeRFs) have recently been explored for continuous wireless channel modeling, existing approaches depend on dense measurements or external priors such as known geometry, visual context, or angle-of-arrival (AoA) information, limiting their practicality in real-world deployments. We present WiNeRF, a neural field framework that learns a spatially continuous, complex-valued wireless channel representation directly from sparse channel state information (CSI) collected by commodity WiFi devices. WiNeRF embeds intrinsic system constraints, such as antenna geometry, limited spatial resolution, and phase uncertainty, as inductive biases through a 3D conical wave sampling model, a multi-resolution implicit scene representation, and a differentiable optimization framework for complex-valued channel learning. Across diverse indoor environments with non-line-of-sight (NLoS) regions, WiNeRF achieves a median prediction SNR of 5.3 dB, outperforming prior neural baselines by 4.9 dB on average (approximately 3x higher prediction SNR), and produces a task-agnostic channel representation that can be directly reused in standard signal-processing pipelines, including beamforming, AoA estimation, and RSSI coverage mapping, without modifying existing hardware or wireless protocols.
