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Obstructed Vehicle-to-Vehicle Channel Modeling for Intelligent Vehicular Communications

Abstract: 

In order to realize the vision of intelligent connected vehicles, it is necessary to model the vehicle-to-vehicle (V2V) channels in various realistic environments, especially when the line-of-sight (LOS) between transmitter (Tx) and receiver (Rx) is obstructed. In this paper, we model obstructed vehicle-to-vehicle (V2V) channels for the 5-GHz band through measurement-validated ray-tracing (RT) simulations. To begin, we establish a realistic V2V RT simulator through integrating three key channel features: small-scale structures (e.g. lampposts, traffic signs), handled by their approximate radar cross sections; large-scale structures (such as buildings and ground), calibrating their electromagnetic and scattering parameters; and obstructing vehicle effects via V2V channel measurements. Then, based on extensive RT simulations, the target channels are characterized comprehensively, in terms of path loss, shadow fading, root-mean-square delay spread, Rician $K$-factor, azimuth/elevation angular spread of arrival/departure, cross-polarization ratio, and their cross-correlations. All the parameters are input into and verified by the 3GPP-like quasi-deterministic radio channel generator (QuaDRiGa). By adding the obstructed V2V scenario into standard channel model families, this paper provides a foundation for evaluating intelligent vehicular communications in challenging conditions.

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Paper Details

Authors:
Ke Guan, Bo Ai, Danping He, David W. Matolak, Qi Wang, Zhangdui Zhong, Thomas Kuerner
Submitted On:
18 November 2018 - 2:17am
Short Link:
Type:
Presentation Slides
Event:
Presenter's Name:
Ke Guan
Paper Code:
1226
Document Year:
2018
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Document Files

Obstructed V2V channel modeling for intelligent vehicular communications_BJTU.pdf

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[1] Ke Guan, Bo Ai, Danping He, David W. Matolak, Qi Wang, Zhangdui Zhong, Thomas Kuerner, "Obstructed Vehicle-to-Vehicle Channel Modeling for Intelligent Vehicular Communications", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3673. Accessed: Jul. 18, 2019.
@article{3673-18,
url = {http://sigport.org/3673},
author = {Ke Guan; Bo Ai; Danping He; David W. Matolak; Qi Wang; Zhangdui Zhong; Thomas Kuerner },
publisher = {IEEE SigPort},
title = {Obstructed Vehicle-to-Vehicle Channel Modeling for Intelligent Vehicular Communications},
year = {2018} }
TY - EJOUR
T1 - Obstructed Vehicle-to-Vehicle Channel Modeling for Intelligent Vehicular Communications
AU - Ke Guan; Bo Ai; Danping He; David W. Matolak; Qi Wang; Zhangdui Zhong; Thomas Kuerner
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3673
ER -
Ke Guan, Bo Ai, Danping He, David W. Matolak, Qi Wang, Zhangdui Zhong, Thomas Kuerner. (2018). Obstructed Vehicle-to-Vehicle Channel Modeling for Intelligent Vehicular Communications. IEEE SigPort. http://sigport.org/3673
Ke Guan, Bo Ai, Danping He, David W. Matolak, Qi Wang, Zhangdui Zhong, Thomas Kuerner, 2018. Obstructed Vehicle-to-Vehicle Channel Modeling for Intelligent Vehicular Communications. Available at: http://sigport.org/3673.
Ke Guan, Bo Ai, Danping He, David W. Matolak, Qi Wang, Zhangdui Zhong, Thomas Kuerner. (2018). "Obstructed Vehicle-to-Vehicle Channel Modeling for Intelligent Vehicular Communications." Web.
1. Ke Guan, Bo Ai, Danping He, David W. Matolak, Qi Wang, Zhangdui Zhong, Thomas Kuerner. Obstructed Vehicle-to-Vehicle Channel Modeling for Intelligent Vehicular Communications [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3673