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Point Cloud Environment-Based Channel Knowledge Map Construction
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DOI：10.1109/tccn.2026.3706507.png)
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Abstract
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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 clouds that contain environmental information relevant to multipath channel gains, by constructing a set of co-focal ellipsoids based on different time of arrival (ToA) values. Then, we train 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., for radio map, it achieves an RMSE of 1.04 dB, outperforming the Kriging interpolation method with an RMSE of 1.68 dB.
Keywords:
Channel knowledge map (CKM)
point cloud
ray-tracing
power delay profile (PDP)
radio map
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Cited Papers
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