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Geometry-Based Vehicle-to-Vehicle Channel Modeling for Large-Scale Simulation

Authors :
Joao Barros
Mate Boban
Ozan K. Tonguz
Source :
IEEE Transactions on Vehicular Technology. 63:4146-4164
Publication Year :
2014
Publisher :
Institute of Electrical and Electronics Engineers (IEEE), 2014.

Abstract

Due to the dynamic nature of vehicular traffic and the road surroundings, vehicle-to-vehicle (V2V) propagation characteristics vary greatly on both small- and large-scale. Recent measurements have shown that both large static objects (e.g., buildings and foliage) as well as mobile objects (surrounding vehicles) have a profound impact on V2V communication. At the same time, system-level Vehicular Ad Hoc Network (VANET) simulators by and large employ simple statistical propagation models, which do not account for surrounding objects explicitly. We designed GEMV$^2$ (Geometry-based Efficient propagation Model for V2V communication), which uses outlines of vehicles, buildings, and foliage to distinguish the following three types of links: line of sight (LOS), non-LOS due to vehicles, and non- LOS due to static objects. For each link, GEMV$^2$ calculates the large-scale signal variations deterministically, whereas the small- scale signal variations are calculated stochastically based on the number and size of surrounding objects. We implement GEMV$^2$ in MATLAB and show that it scales well by using it to simulate radio propagation for city-wide networks with tens of thousands of vehicles on commodity hardware. We make the source code of GEMV$^2$ freely available. Finally, we validate GEMV$^2$ against extensive measurements performed in urban, suburban, highway, and open space environment.<br />Preprint of an article that will be published in IEEE Transactions on Vehicular Technology

Details

ISSN :
19399359 and 00189545
Volume :
63
Database :
OpenAIRE
Journal :
IEEE Transactions on Vehicular Technology
Accession number :
edsair.doi.dedup.....05bc5bb55c6b7a40cb746d696fd7f6dc
Full Text :
https://doi.org/10.1109/tvt.2014.2317803