Sparse Measurement Data Driven Air-to-Ground Path Loss Prediction over Vegetation Area

Hanpeng Li; Xiaomin Chen; Kai Mao; Fuqiao Duan; Yanheng Qiu; Qiuming Zhu; Boyu Hua; Farman Ali

doi:10.1109/VTC2022-Fall57202.2022.10012768

Sparse Measurement Data Driven Air-to-Ground Path Loss Prediction over Vegetation Area

Hanpeng Li
, Xiaomin Chen^*
, Kai Mao
, Fuqiao Duan
, Yanheng Qiu
, Qiuming Zhu^*
, Boyu Hua
, Farman Ali

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

7 Scopus citations

Abstract

In this paper, a novel path loss (PL) prediction model is proposed for the obstructed-line-of-sight (OLoS) and non-line-of-sight (NLoS) paths in unmanned aerial vehicle (UAV) communication over vegetation areas. The proposed PL prediction model is designed based on a deep neural network (DNN) with a pre-training module (PTM). We pre-train the DNN by ray tracing (RT) simulation data and then optimize the network by sparse measurement data, which can significantly reduce the demand for measurement data. Moreover, PL measurements over vegetation areas are carried out at 2 GHz on the campus to validate the proposed model. It is shown that the prediction results of the proposed model are in good agreement with the measurement data and the ones of the fitted International Telecommunication Union recommendation (FITU-R) model under the OLoS case. Moreover, the proposed model is more general and suitable for air-to-ground (A2G) communications by considering the impact of the wide range of reflection angle (RA) variations on the PL.

Original language	English
Title of host publication	2022 IEEE 96th Vehicular Technology Conference, VTC 2022-Fall 2022 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665454681
DOIs	https://doi.org/10.1109/VTC2022-Fall57202.2022.10012768
State	Published - 2022
Externally published	Yes
Event	96th IEEE Vehicular Technology Conference, VTC 2022-Fall - London, United Kingdom Duration: 26 Sep 2022 → 29 Sep 2022

Publication series

Name	IEEE Vehicular Technology Conference
Volume	2022-September
ISSN (Print)	1090-3038

Conference

Conference	96th IEEE Vehicular Technology Conference, VTC 2022-Fall
Country/Territory	United Kingdom
City	London
Period	26/09/22 → 29/09/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

Unmanned aerial vehicle (UAV)
deep neural network (DNN)
path loss (PL)
reflection angle (RA)
vegetation area

ASJC Scopus subject areas

Computer Science Applications
Applied Mathematics
Electrical and Electronic Engineering

Access to Document

10.1109/VTC2022-Fall57202.2022.10012768

Cite this

Li, H., Chen, X., Mao, K., Duan, F., Qiu, Y., Zhu, Q., Hua, B., & Ali, F. (2022). Sparse Measurement Data Driven Air-to-Ground Path Loss Prediction over Vegetation Area. In 2022 IEEE 96th Vehicular Technology Conference, VTC 2022-Fall 2022 - Proceedings (IEEE Vehicular Technology Conference; Vol. 2022-September). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/VTC2022-Fall57202.2022.10012768

@inproceedings{1c169ed6a765400f9ef977373edd7a33,

title = "Sparse Measurement Data Driven Air-to-Ground Path Loss Prediction over Vegetation Area",

abstract = "In this paper, a novel path loss (PL) prediction model is proposed for the obstructed-line-of-sight (OLoS) and non-line-of-sight (NLoS) paths in unmanned aerial vehicle (UAV) communication over vegetation areas. The proposed PL prediction model is designed based on a deep neural network (DNN) with a pre-training module (PTM). We pre-train the DNN by ray tracing (RT) simulation data and then optimize the network by sparse measurement data, which can significantly reduce the demand for measurement data. Moreover, PL measurements over vegetation areas are carried out at 2 GHz on the campus to validate the proposed model. It is shown that the prediction results of the proposed model are in good agreement with the measurement data and the ones of the fitted International Telecommunication Union recommendation (FITU-R) model under the OLoS case. Moreover, the proposed model is more general and suitable for air-to-ground (A2G) communications by considering the impact of the wide range of reflection angle (RA) variations on the PL.",

keywords = "Unmanned aerial vehicle (UAV), deep neural network (DNN), path loss (PL), reflection angle (RA), vegetation area",

author = "Hanpeng Li and Xiaomin Chen and Kai Mao and Fuqiao Duan and Yanheng Qiu and Qiuming Zhu and Boyu Hua and Farman Ali",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 96th IEEE Vehicular Technology Conference, VTC 2022-Fall ; Conference date: 26-09-2022 Through 29-09-2022",

year = "2022",

doi = "10.1109/VTC2022-Fall57202.2022.10012768",

language = "English",

series = "IEEE Vehicular Technology Conference",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2022 IEEE 96th Vehicular Technology Conference, VTC 2022-Fall 2022 - Proceedings",

address = "United States",

}

Li, H, Chen, X, Mao, K, Duan, F, Qiu, Y, Zhu, Q, Hua, B & Ali, F 2022, Sparse Measurement Data Driven Air-to-Ground Path Loss Prediction over Vegetation Area. in 2022 IEEE 96th Vehicular Technology Conference, VTC 2022-Fall 2022 - Proceedings. IEEE Vehicular Technology Conference, vol. 2022-September, Institute of Electrical and Electronics Engineers Inc., 96th IEEE Vehicular Technology Conference, VTC 2022-Fall, London, United Kingdom, 26/09/22. https://doi.org/10.1109/VTC2022-Fall57202.2022.10012768

Sparse Measurement Data Driven Air-to-Ground Path Loss Prediction over Vegetation Area. / Li, Hanpeng; Chen, Xiaomin; Mao, Kai et al.
2022 IEEE 96th Vehicular Technology Conference, VTC 2022-Fall 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2022. (IEEE Vehicular Technology Conference; Vol. 2022-September).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Sparse Measurement Data Driven Air-to-Ground Path Loss Prediction over Vegetation Area

AU - Li, Hanpeng

AU - Chen, Xiaomin

AU - Mao, Kai

AU - Duan, Fuqiao

AU - Qiu, Yanheng

AU - Zhu, Qiuming

AU - Hua, Boyu

AU - Ali, Farman

PY - 2022

Y1 - 2022

N2 - In this paper, a novel path loss (PL) prediction model is proposed for the obstructed-line-of-sight (OLoS) and non-line-of-sight (NLoS) paths in unmanned aerial vehicle (UAV) communication over vegetation areas. The proposed PL prediction model is designed based on a deep neural network (DNN) with a pre-training module (PTM). We pre-train the DNN by ray tracing (RT) simulation data and then optimize the network by sparse measurement data, which can significantly reduce the demand for measurement data. Moreover, PL measurements over vegetation areas are carried out at 2 GHz on the campus to validate the proposed model. It is shown that the prediction results of the proposed model are in good agreement with the measurement data and the ones of the fitted International Telecommunication Union recommendation (FITU-R) model under the OLoS case. Moreover, the proposed model is more general and suitable for air-to-ground (A2G) communications by considering the impact of the wide range of reflection angle (RA) variations on the PL.

AB - In this paper, a novel path loss (PL) prediction model is proposed for the obstructed-line-of-sight (OLoS) and non-line-of-sight (NLoS) paths in unmanned aerial vehicle (UAV) communication over vegetation areas. The proposed PL prediction model is designed based on a deep neural network (DNN) with a pre-training module (PTM). We pre-train the DNN by ray tracing (RT) simulation data and then optimize the network by sparse measurement data, which can significantly reduce the demand for measurement data. Moreover, PL measurements over vegetation areas are carried out at 2 GHz on the campus to validate the proposed model. It is shown that the prediction results of the proposed model are in good agreement with the measurement data and the ones of the fitted International Telecommunication Union recommendation (FITU-R) model under the OLoS case. Moreover, the proposed model is more general and suitable for air-to-ground (A2G) communications by considering the impact of the wide range of reflection angle (RA) variations on the PL.

KW - Unmanned aerial vehicle (UAV)

KW - deep neural network (DNN)

KW - path loss (PL)

KW - reflection angle (RA)

KW - vegetation area

UR - https://www.scopus.com/pages/publications/85147044222

U2 - 10.1109/VTC2022-Fall57202.2022.10012768

DO - 10.1109/VTC2022-Fall57202.2022.10012768

M3 - Conference contribution

AN - SCOPUS:85147044222

T3 - IEEE Vehicular Technology Conference

BT - 2022 IEEE 96th Vehicular Technology Conference, VTC 2022-Fall 2022 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 96th IEEE Vehicular Technology Conference, VTC 2022-Fall

Y2 - 26 September 2022 through 29 September 2022

ER -

Sparse Measurement Data Driven Air-to-Ground Path Loss Prediction over Vegetation Area

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this