Abstract
Inter-vehicle communication promises to prevent accidents by enabling applications such as cross-traffic assistance. This application requires information from vehicles in non-line-of-sight (NLOS) areas due to building at intersection corners. The periodic cooperative awareness messages are foreseen to be sent via 5.9 GHz IEEE 802.11p. While it is known that existing micro-cell models might not apply well, validated propagation models for vehicular 5.9 GHz NLOS conditions are still missing. In this article, we develop a 5.9 GHz NLOS path-loss and fading model based on real-world measurements at a representative selection of intersections in the city of Munich. We show that (a) the measurement data can very well be fitted to an analytical model, (b) the model incorporates specific geometric aspects in closed-form as well as normally distributed fading in NLOS conditions, and (c) the model is of low complexity, thus, could be used in large-scale packet-level simulations. A comparison to existing micro-cell models shows that our model significantly differs.
Highlights
Vehicular communication is envisioned to increase range and coverage of location and behavior awareness of vehicles, enabling highly developed pro-active safety systems.The idea is that all vehicles communicate information like position, speed, and heading periodically to other vehicles in cooperative awareness messages to enable the derivation of an environment picture, used as basis for movement prediction
We showed in [17] that this averaging is viable, as the performance is very similar despite the transmitter being in the different side streets
At LOS on the crossing street, either the normal LOS path-loss should be used with distance as dt + dr or a percental value between LOS at intersection center and NLOS value at the first point of NLOS
Summary
Vehicular communication is envisioned to increase range and coverage of location and behavior awareness of vehicles, enabling highly developed pro-active safety systems. Since the classification is based on nearest building vertex, and front gardens (with bushes and trees) can further limit the field of view, we suspected suburban intersections to provide worse reception conditions at similar distances into the crossing street compared to urban ones. We generated reception power/rate versus rcv↔center distance plots and a map-based result visualization for each run, available at [19] We limited the fit input to 20
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