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

TL;DR

This paper introduces GEMV$^2$, a geometry-based V2V channel model that explicitly accounts for surrounding objects to improve large-scale simulation accuracy, validated through extensive real-world measurements.

Contribution

GEMV$^2$ is a novel geometry-based model that explicitly incorporates static and mobile objects for more accurate V2V channel simulation.

Findings

GEMV$^2$ accurately models large-scale signal variations.

The model scales efficiently to city-wide networks.

Validation shows good agreement with real measurements.

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.