Projekt per år
Realistic network simulations are necessary to assess the performance of any communication system. In this paper, we describe an implementation of a channel model for vehicle-to-vehicle (V2V) communication in the OMNeT++/Plexe simulation environment. The model is based on previous extensive measurements in a V2V multilink highway scenario and cover line-of-sight (LOS) as well as obstructed LOS (OLOS) scenarios, which occurs when one or more vehicles obstruct the LOS component. The implementation captures both the temporal autocorrelation and the joint multilink cross-correlation processes to achieve a realistic behavior. Preliminary results show that the implementation now generates stochastic large-scale fading with an autocorrelation function that agrees well with measured data. A representation of the cross-correlation process is now implemented through proper channel model selection since the geometry and location of objects are known in Plexe. We also show the impact of the suggested V2V physical layer (PHY) on the performance evaluation results observed at the facilities layer. As a metric, we use the data age, which is a measure how old the information about a vehicle is. When considering the autocorrelation in simulations, the experienced data-age increases. Examples show an increase of the 10% percentile data-age from 0.1s to 1.5s, which may affect the application performance significantly in critical situations.
|Titel på värdpublikation||2018 IEEE 87th Vehicular Technology Conference, VTC Spring 2018 - Proceedings|
|Förlag||IEEE - Institute of Electrical and Electronics Engineers Inc.|
|Status||Published - 2018 juli 20|
|Evenemang||87th IEEE Vehicular Technology Conference, VTC Spring 2018 - Porto, Portugal|
Varaktighet: 2018 juni 3 → 2018 juni 6
|Namn||Vehicular Technology Conference|
|Konferens||87th IEEE Vehicular Technology Conference, VTC Spring 2018|
|Period||2018/06/03 → 2018/06/06|
FingeravtryckUtforska forskningsämnen för ”Geometry based channel models with cross- and autocorrelation for vehicular network simulations”. Tillsammans bildar de ett unikt fingeravtryck.
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