标题： 基于点云环境的信道知识地图构建 显示英文标题

标题： Point Cloud Environment-Based Channel Knowledge Map Construction

摘要： 信道知识图谱（CKM）为感兴趣区域提供一定层次的信道状态信息（CSI），通过减少频繁CSI获取的开销，成为环境感知通信的关键使能技术。 然而，现有的CKM构建方案采用了过于简化的环境信息，这显著影响了其准确性。 为了解决这个问题，本工作提出了一种结合模型和数据驱动的方法，通过利用点云环境数据以及少量带有位置标签的信道信息样本来构建CKM。 首先，我们提出了一种新颖的点选择器，通过基于不同到达时间（ToAs）构造一组共焦椭球体，来识别包含与多径信道增益相关的环境信息的点云子集。 然后，我们训练了一个神经信道增益估计器，使用我们在实地测量中收集的真实世界数据集，包括环境点云和相应的信道数据，以学习每个选定子集与其对应信道增益之间的映射关系。 最后，实验结果表明：对于功率时延轮廓（PDP）的CKM构建，所提出的方法实现了2.95 dB的均方根误差（RMSE），明显低于传统射线追踪方法实现的7.32 dB；对于接收功率值的CKM构建，即无线图谱，它实现了1.04 dB的RMSE，超过了Kriging插值方法实现的1.68 dB的RMSE。 显示英文摘要

摘要： Channel knowledge map (CKM) provides certain levels of channel state information (CSI) for an area of interest, serving as a critical enabler for environment-aware communications by reducing the overhead of frequent CSI acquisition. However, existing CKM construction schemes adopt over-simplified environment information, which significantly compromises their accuracy. To address this issue, this work proposes a joint model- and data-driven approach to construct CKM by leveraging point cloud environmental data along with a few samples of location-tagged channel information. First, we propose a novel point selector to identify subsets of point cloud that contain environmental information relevant to multipath channel gains, by constructing a set of co-focal ellipsoids based on different time of arrival (ToAs). Then, we trained a neural channel gain estimator to learn the mapping between each selected subset and its corresponding channel gain, using a real-world dataset we collected through field measurements, comprising environmental point clouds and corresponding channel data. Finally, experimental results demonstrate that: For CKM construction of power delay profile (PDP), the proposed method achieves a root mean squared error (RMSE) of 2.95 dB, significantly lower than the 7.32 dB achieved by the conventional ray-tracing method; for CKM construction of received power values, i.e., radio map, it achieves an RMSE of 1.04 dB, surpassing the Kriging interpolation method with an RMSE of 1.68 dB.