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A Path Loss and Shadowing Model for Multilink Vehicle-to-Vehicle Channels in Urban Intersections

Overview
Journal Sensors (Basel)
Publisher MDPI
Specialty Biotechnology
Date 2018 Dec 19
PMID 30558221
Citations 4
Authors
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Abstract

The non line-of-sight (NLOS) scenario in urban intersections is critical in terms of traffic safety-a scenario where Vehicle-to-Vehicle (V2V) communication really can make a difference by enabling communication and detection of vehicles around building corners. A few NLOS V2V channel models exist in the literature but they all have some form of limitation, and therefore further research is need. In this paper, we present an alternative NLOS path loss model based on analysis from measured V2V communication channels at 5.9 GHz between six vehicles in two urban intersections. We analyze the auto-correlation of the large scale fading process and the influence of the path loss model on this. In cases where a proper model for the path loss and the antenna pattern is included, the de-correlation distance for the auto-correlation is as low as 2⁻4 m, and the cross-correlation for the large scale fading between different links can be neglected. Otherwise, the de-correlation distance has to be much longer and the cross-correlation between the different communication links needs to be considered separately, causing the computational complexity to be unnecessarily large. With these findings, we stress that vehicular ad-hoc network (VANET) simulations should be based on the current geometry, i.e., a proper path loss model should be applied depending on whether the V2V communication is blocked or not by other vehicles or buildings.

Citing Articles

Path loss modeling based on neural networks and ensemble method for future wireless networks.

Elmezughi M, Salih O, Afullo T, Duffy K Heliyon. 2023; 9(9):e19685.

PMID: 37809436 PMC: 10558953. DOI: 10.1016/j.heliyon.2023.e19685.


Neuroevolution-Based Adaptive Antenna Array Beamforming Scheme to Improve the V2V Communication Performance at Intersections.

Kang Kim H, Becerra R, Bolufe S, Azurdia-Meza C, Montejo-Sanchez S, Zabala-Blanco D Sensors (Basel). 2021; 21(9).

PMID: 33922529 PMC: 8122899. DOI: 10.3390/s21092956.


Deterministic 3D Ray-Launching Millimeter Wave Channel Characterization for Vehicular Communications in Urban Environments.

Rodriguez-Corbo F, Azpilicueta L, Celaya-Echarri M, Lopez-Iturri P, Picallo I, Falcone F Sensors (Basel). 2020; 20(18).

PMID: 32947776 PMC: 7570788. DOI: 10.3390/s20185284.


Path Loss Prediction based on Machine Learning Techniques: Principal Component Analysis, Artificial Neural Network and Gaussian Process.

Jo H, Park C, Lee E, Choi H, Park J Sensors (Basel). 2020; 20(7).

PMID: 32235640 PMC: 7181246. DOI: 10.3390/s20071927.

References
1.
Granda F, Azpilicueta L, Vargas-Rosales C, Lopez-Iturri P, Aguirre E, Astrain J . Spatial Characterization of Radio Propagation Channel in Urban Vehicle-to-Infrastructure Environments to Support WSNs Deployment. Sensors (Basel). 2017; 17(6). PMC: 5492156. DOI: 10.3390/s17061313. View