{'fraction_non_alphanumeric': 0.0221606648199446, 'fraction_numerical': 0.0034626038781163434, 'mean_word_length': 5.509009009009009, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Millimeter-wave Received Power Prediction Using Point Cloud Data and Supervised Learning

This paper demonstrates the feasibility of predicting the future received power of millimeter-wave (mmWave) communication using point cloud data. To mitigate the human blockage problem in mmWave communication, previous works have studied a camera-vision assisted mmWave link quality prediction, which predicts the time-series of the received power from the next moment to as many as several hundred milliseconds ahead by leveraging camera imagery and machine learning. However, camera imagery generally includes privacy-sensitive information, which induces privacy concerns in the camera-assisted mmWave networks. In this paper, we demonstrate that point cloud, which can be obtained by light detection and ranging (LiDAR) sensors and poses fewer privacy concerns than camera imagery, can be an alternative to the cameras in mmWave link quality prediction. Specifically, we propose a mmWave received power prediction method using point cloud data, and our experimental evaluation demonstrates that the proposed method predicts mmWave received power 500ms ahead with a root-mean-squared error of 3.3dB, which is comparable to the existing camera-based method. Moreover, we verify that even minor environmental changes can degrade the accuracy of the trained prediction model and this degradation can be mitigated by model fine-tuning with a small dataset.