Spatial Frequency-based Feature Extraction for Point Cloud-based Proactive mmWave Link Quality Prediction

This paper proposes a feature extraction method from three-dimensional (3D) point cloud employing spatial frequency analysis for point cloud-based link quality prediction. The proposed method aims to address the human blockage problem in millimeter-wave (mmWave) communications, where proactive communication control through machine learning-based future link quality prediction has been shown to be effective in preventing link quality degradation. However, the use of 3D point clouds presents challenges such as increased transmission cost and computational complexity due to the large data size. To address these challenges, we propose a preprocessing method that can efficiently extract features from the point cloud and reduce data size without compromising the accuracy of mmWave link quality prediction. Our approach uses spatial frequency-based filtering to isolate signals related to moving obstacles, such as pedestrians, in the frequency domain. It also removes large, static background objects like walls and furniture. The proposed method can extract relevant objects in the point cloud based on their spatial frequency without requiring object detection or segmentation. The experimental results demonstrate that our proposed method significantly reduces feature data size by approximately 99% compared to conventional methods, while still maintaining high link quality prediction accuracy.