Measurement-Based Prediction of mmWave Channel Parameters Using Deep Learning and Point Cloud

Published

August 2, 2024

Author(s)

Anuraag Bodi, Raied Caromi, Jian Wang, Jelena Senic, Camillo Gentile, Hang Mi, Bo Ai, Ruisi He

Abstract

Millimeter-wave (MmWave) channel characteristics are quite different from sub-6 GHz frequency bands. The major differences include higher path loss and sparser multipath components (MPCs), resulting in more significant time-varying characteristics in mmWave channels. It is difficult to characterize the time-varying characteristics of mmWave channels through statistical models, e.g. slope-intercept models for path loss and lognormal models for delay spread and angular spread. Therefore, highly accurate channel modeling and prediction are necessary for deployment of mmWave communication systems. In this paper, a mmWave channel parameter prediction method using deep learning and environment point cloud is proposed. The parameters predicted include path loss, root-mean-square (RMS) delay spread, angular spread and Rician K factor. First, we introduce a novel measurement campaign for indoor mmWave channel at 60 GHz, where a light detection and ranging (LiDAR) sensor and panoramic camera were co-located with a channel sounder and then time-synchronized point clouds and images were captured to describe environmental information. Furthermore, a fusion method between the point clouds and images is proposed based on geometric relationship between the LiDAR and camera, to compress the size of the data collected. Second, based on a classic point cloud classification model (PointNet), we propose a novel regression PointNet model applied to channel parameter prediction. To overcome generalization problem of model under limited measurements, an area-by-area training and testing method is proposed. Third, we evaluate the proposed prediction model and training method, by comparing prediction results with measured ground truth. To provide insights on what training inputs are best, we demonstrate the impacts of different combinations of input information on prediction accuracy. Last, the deployment and implementation method of the proposed model is recommended to the readers.

Citation

IEEE Open Journal of Vehicular Technology

Pub Type

Journals

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Keywords

Channel measurement, channel prediction, deep learning, mmWave channel, point cloud

Citation

Bodi, A. , Caromi, R. , Wang, J. , Senic, J. , Gentile, C. , Mi, H. , Ai, B. and He, R. (2024), Measurement-Based Prediction of mmWave Channel Parameters Using Deep Learning and Point Cloud, IEEE Open Journal of Vehicular Technology, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=956227 (Accessed July 13, 2025)