Millimeter-wave Received Power Prediction Using Point Cloud Data and Supervised Learning
Millimeter-wave Received Power Prediction Using Point Cloud Data and Supervised Learning
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DOI：
10.1109/vtc2022-spring54318.2022.9860728
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发表时间：
2022-06
期刊：
影响因子：
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通讯作者：
Shoki Ohta;T. Nishio;R. Kudo;Kahoko Takahashi
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文献类型：
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作者：
Shoki Ohta;T. Nishio;R. Kudo;Kahoko Takahashi
本文论证了利用点云数据预测未来毫米波通信接收功率的可行性。为了缓解毫米波通信中的人为阻塞问题，以前的工作已经研究了摄像机视觉辅助的毫米波链路质量预测，该预测通过利用摄像机图像和机器学习来预测从下一时刻到多达几百毫秒的接收功率的时间序列。然而，相机图像通常包括隐私敏感信息，这在相机辅助的毫米波网络中引起隐私问题。在本文中，我们证明了点云，它可以通过光检测和测距（LiDAR）传感器获得，并构成比相机图像更少的隐私问题，可以替代相机在毫米波链路质量预测。具体来说，我们提出了一种使用点云数据的毫米波接收功率预测方法，我们的实验评估表明，所提出的方法预测毫米波接收功率提前500 ms，均方根误差为3.3dB，这是与现有的基于相机的方法。此外，我们还验证了即使是微小的环境变化也会降低训练预测模型的准确性，这种降低可以通过使用小数据集进行模型微调来缓解。
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.