IEEE Transactions on Vehicular Technology · 2014 · 219 citations · 35 references
EngineeringCommunicationElectrical EngineeringGeometric ModelingPropagationWireless CommunicationsIntelligent Transportation SystemsPropagation ModelingVehicle Communication NetworksVehicle ModelingVehicle-to-everything CommunicationWireless Channel ModelingGeometry-based Channel ModelingRadio Wave Propagation EffectsV2v CommunicationVehicle-to-vehicle Channel ModelsVehicle-to-vehicle CommunicationVehicular Ad Hoc NetworksVehicular Channel ModelingWireless Vehicle Networks
V2V propagation varies greatly due to traffic dynamics and surrounding static and mobile objects, yet most VANET simulators rely on simple statistical models that ignore these effects. We designed GEMV$^2$, a geometry‑based efficient propagation model that uses vehicle, building, and foliage outlines to classify links as LOS, NLOS‑vehicle, or NLOS‑static. GEMV$^2$ deterministically computes large‑scale signal variations per link type and stochastically models small‑scale variations based on surrounding objects, and is implemented in MATLAB, scaling to city‑wide networks with tens of thousands of vehicles on commodity hardware. The model is freely available and has been validated against extensive measurements in urban, suburban, highway, and open‑space environments.
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.
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OpenStreetMap: User-Generated Street Maps
Muki Haklay, Patrick Weber · IEEE Pervasive Computing · 2008 · 3.1K citations · Full text
Antonin Guttman · ACM SIGMOD Record · 1984 · 1.6K citations · Full text