• DocumentCode
    3716569
  • Title

    Empirical Performance Models for V2V Communications

  • Author

    Miguel Sepulcre;Javier Gozalvez;Baldomero Coll-Perales; Lucas-Estañ;Jose Ramon Gisbert

  • Author_Institution
    Ubiquitous Wireless Commun. Res. Lab., Univ. Miguel Hernandez de Elche, Elche, Spain
  • fYear
    2015
  • Firstpage
    737
  • Lastpage
    742
  • Abstract
    Vehicular networks will significantly enhance traffic safety and management thanks to the wireless exchange of messages between vehicles and between vehicles and infrastructure nodes. Vehicular networks will make use of the IEEE 802.11p technology in the 5.9GHz frequency band. Simulation studies are generally utilized to analyze the performance of vehicular communications and networking protocols. The impact of these studies can be significantly influenced by the accuracy of the employed models, in particular at the radio and physical level. In this context, this paper proposes a set of 48 computationally inexpensive empirical performance models for V2V (Vehicle-to-Vehicle) communications. The models have been derived from an extensive V2V measurement campaign, and model the PDR (Packet Delivery Ratio) and PSR (Packet Sensing Ratio) as a function of the distance between transmitter and receiver. The set of models include PDR and PSR curves for urban and highway environments considering three different transmission power levels and eight data rates (modulation and coding schemes). The proposed models can be easily integrated into communications and networking simulators.
  • Keywords
    "Computational modeling","Vehicles","Data models","Mathematical model","Receivers","Load modeling","Protocols"
  • Publisher
    ieee
  • Conference_Titel
    Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing (CIT/IUCC/DASC/PICOM), 2015 IEEE International Conference on
  • Type

    conf

  • DOI
    10.1109/CIT/IUCC/DASC/PICOM.2015.108
  • Filename
    7363148