A Dynamic Wideband Directional Channel Model for Vehicle-to-Vehicle Communications

Tutkimustuotos: Lehtiartikkelivertaisarvioitu


  • Ruisi He
  • Olivier Renaudin
  • Veli Matti Kolmonen
  • Katsuyuki Haneda
  • Zhangdui Zhong
  • Bo Ai
  • Claude Oestges


  • Beijing Jiaotong University
  • University of Southern California
  • Nokia
  • Universite Catholique de Louvain


Vehicle-to-vehicle (V2V) communications have received a lot of attention due to their numerous applications in traffic safety. The design, testing, and improvement of the V2V system hinge critically on the understanding of the propagation channels. An important feature of the V2V channel is the time variance. To statistically model the time-variant V2V channels, a dynamic wideband directional channel model is proposed in this paper, based on measurements conducted at 5.3 GHz in suburban, urban, and underground parking environments. The model incorporates both angular and delay domain properties and the dynamic evolution of multipath components (MPCs). The correlation matrix distance is used to determine the size of local wide-sense stationary (WSS) region. Within each WSS time window, MPCs are extracted using the Bartlett beamformer. A multipath distance-based tracking algorithm is used to identify the "birth" and "death" of such paths over different stationarity regions, and the lifetime of MPC is modeled with a truncated Gaussian distribution. Distributions for the number of multipaths and their positions are statistically modeled. Within each path lifetime, the initial power is found to depend on the excess delay, and a linear polynomial function is used to model the variations within the lifetime. In addition, a Nakagami distribution is suggested to describe the fading behavior. Finally, the model implementation is validated by comparison of second-order statistics between measurements and simulations.


JulkaisuIEEE Transactions on Industrial Electronics
TilaJulkaistu - 1 joulukuuta 2015
OKM-julkaisutyyppiA1 Julkaistu artikkeli, soviteltu

ID: 9186423