Vehicular Transmission Reliability over Blind Intersections

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Conference Presentation

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ITS, V2X, V2V, C-V2X, DSRC, VANET, 802.11p, 5G, LOS, WLOS, NLOS, CAV, URLLC, H2020, intelligent transportation system, connected vehicles, vehicle-to-everything, vehicle-to-vehicle, dedicated short-range communication, vehicular ad hoc network, path loss, propagation model, channel model, connected vehicle, autonomous vehicle, connected and autonomous vehicle, outage probability, reliability, ultra-reliable low-latency communication, stochastic geometry, meta distribution, road safety, traffic efficiency, urban intersection, suburban intersection, smart intersection, intelligent intersection, smart city, research funding, grant proposals, government funding, European Commission, EU, MSCA, Marie-Curie, Marie Skłodowska Curie Action


Vehicle-to-vehicle (V2V) communication can improve road safety and traffic efficiency, particularly around critical areas such as intersections. We analytically derive V2V success probability near an urban intersection, based on empirically supported line-of-sight (LOS), weak-line-of-sight (WLOS), and non-line-of-sight (NLOS) channel models. The analysis can serve as a preliminary design tool for performance assessment over different system parameters and target performance requirements. The most interesting outcome of this research is the ability to design the network and explicitly quantify the tolerated number of simultaneous transmissions that could occur at the same time-frame of the wanted transmission, while still meeting the predetermined target reliability. Meanwhile, we will also discuss means to determine the fraction of vehicular traffic realizations that achieve the target reliability. This is a more granular finely detailed analysis, and it will basically build on the results presented earlier.


Faculty of Applied Science and Technology (FAST)

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Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.


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