Measurement-Based Prediction of mmWave Channel Parameters Using Deep Learning and Point Cloud
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...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Open Journal of Vehicular Technology |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10620622/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832582346557095936 |
|---|---|
| author | Hang Mi Bo Ai Ruisi He Anuraag Bodi Raied Caromi Jian Wang Jelena Senic Camillo Gentile Yang Miao |
| author_facet | Hang Mi Bo Ai Ruisi He Anuraag Bodi Raied Caromi Jian Wang Jelena Senic Camillo Gentile Yang Miao |
| author_sort | Hang Mi |
| collection | DOAJ |
| description | 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 <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula> 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. |
| format | Article |
| id | doaj-art-eeb412f3d58a462582190dd0bdbf03f7 |
| institution | Kabale University |
| issn | 2644-1330 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Vehicular Technology |
| spelling | doaj-art-eeb412f3d58a462582190dd0bdbf03f72025-01-30T00:04:11ZengIEEEIEEE Open Journal of Vehicular Technology2644-13302024-01-0151059107210.1109/OJVT.2024.343685710620622Measurement-Based Prediction of mmWave Channel Parameters Using Deep Learning and Point CloudHang Mi0https://orcid.org/0000-0001-5792-3103Bo Ai1https://orcid.org/0000-0001-6850-0595Ruisi He2https://orcid.org/0000-0003-4135-3227Anuraag Bodi3Raied Caromi4https://orcid.org/0000-0002-8273-0642Jian Wang5https://orcid.org/0000-0003-4596-1932Jelena Senic6https://orcid.org/0000-0003-3081-5860Camillo Gentile7https://orcid.org/0000-0002-0660-8215Yang Miao8https://orcid.org/0000-0003-4007-7478State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, ChinaState Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, ChinaState Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, ChinaAssociate, National Institute of Standards and Technology, Prometheus Computing LLC, Gaithersburg, MD, USAWireless Networks Division, National Institute of Standards and Technology, Gaithersburg, MD, USAWireless Networks Division, National Institute of Standards and Technology, Gaithersburg, MD, USAWireless Networks Division, National Institute of Standards and Technology, Gaithersburg, MD, USAWireless Networks Division, National Institute of Standards and Technology, Gaithersburg, MD, USAEEMCS faculty, University of Twente, Enschede, The NetherlandsMillimeter-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 <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula> 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.https://ieeexplore.ieee.org/document/10620622/Channel measurementchannel predictiondeep learningmmWave channelpoint cloud |
| spellingShingle | Hang Mi Bo Ai Ruisi He Anuraag Bodi Raied Caromi Jian Wang Jelena Senic Camillo Gentile Yang Miao Measurement-Based Prediction of mmWave Channel Parameters Using Deep Learning and Point Cloud IEEE Open Journal of Vehicular Technology Channel measurement channel prediction deep learning mmWave channel point cloud |
| title | Measurement-Based Prediction of mmWave Channel Parameters Using Deep Learning and Point Cloud |
| title_full | Measurement-Based Prediction of mmWave Channel Parameters Using Deep Learning and Point Cloud |
| title_fullStr | Measurement-Based Prediction of mmWave Channel Parameters Using Deep Learning and Point Cloud |
| title_full_unstemmed | Measurement-Based Prediction of mmWave Channel Parameters Using Deep Learning and Point Cloud |
| title_short | Measurement-Based Prediction of mmWave Channel Parameters Using Deep Learning and Point Cloud |
| title_sort | measurement based prediction of mmwave channel parameters using deep learning and point cloud |
| topic | Channel measurement channel prediction deep learning mmWave channel point cloud |
| url | https://ieeexplore.ieee.org/document/10620622/ |
| work_keys_str_mv | AT hangmi measurementbasedpredictionofmmwavechannelparametersusingdeeplearningandpointcloud AT boai measurementbasedpredictionofmmwavechannelparametersusingdeeplearningandpointcloud AT ruisihe measurementbasedpredictionofmmwavechannelparametersusingdeeplearningandpointcloud AT anuraagbodi measurementbasedpredictionofmmwavechannelparametersusingdeeplearningandpointcloud AT raiedcaromi measurementbasedpredictionofmmwavechannelparametersusingdeeplearningandpointcloud AT jianwang measurementbasedpredictionofmmwavechannelparametersusingdeeplearningandpointcloud AT jelenasenic measurementbasedpredictionofmmwavechannelparametersusingdeeplearningandpointcloud AT camillogentile measurementbasedpredictionofmmwavechannelparametersusingdeeplearningandpointcloud AT yangmiao measurementbasedpredictionofmmwavechannelparametersusingdeeplearningandpointcloud |