  This study demonstrates the feasibility of point cloud-based proactive link
quality prediction for millimeter-wave (mmWave) communications. Previous
studies have proposed machine learning-based methods to predict received signal
strength for future time periods using time series of depth images to mitigate
the line-of-sight (LOS) path blockage by pedestrians in mmWave communication.
However, these image-based methods have limited applicability due to privacy
concerns as camera images may contain sensitive information. This study
proposes a point cloud-based method for mmWave link quality prediction and
demonstrates its feasibility through experiments. Point clouds represent
three-dimensional (3D) spaces as a set of points and are sparser and less
likely to contain sensitive information than camera images. Additionally, point
clouds provide 3D position and motion information, which is necessary for
understanding the radio propagation environment involving pedestrians. This
study designs the mmWave link quality prediction method and conducts realistic
indoor experiments, where the link quality fluctuates significantly due to
human blockage, using commercially available IEEE 802.11ad-based 60 GHz
wireless LAN devices and Kinect v2 RGB-D camera and Velodyne VLP-16 light
detection and ranging (LiDAR) for point cloud acquisition. The experimental
results showed that our proposed method can predict future large attenuation of
mmWave received signal strength and throughput induced by the LOS path blockage
by pedestrians with comparable or superior accuracy to image-based prediction
methods. Hence, our point cloud-based method can serve as a viable alternative
to image-based methods.
