Point Cloud Environment-Based Channel Knowledge Map Construction

TL;DR

This paper introduces a novel approach combining point cloud environmental data and neural networks to construct more accurate channel knowledge maps for environment-aware communications, significantly outperforming traditional methods.

Contribution

It proposes a joint model- and data-driven method using point cloud data and neural estimation to improve CKM accuracy over existing simplified models.

Findings

Achieves an RMSE of 2.95 dB for power delay profile, outperforming ray-tracing.

Attains an RMSE of 1.04 dB for radio maps, better than Kriging interpolation.

Demonstrates effectiveness with real-world measurement data.

Abstract

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.