A Stochastic Geometry Approach to the Modeling of DSRC for Vehicular Safety Communication Article

Tong, Z, Lu, H, Haenggi, M et al. (2016). A Stochastic Geometry Approach to the Modeling of DSRC for Vehicular Safety Communication . IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 17(5), 1448-1458. 10.1109/TITS.2015.2507939

cited authors

  • Tong, Z; Lu, H; Haenggi, M; Poellabauer, C

authors

abstract

  • Vehicle-to-vehicle safety communications based on the dedicated short-range communication technology have the potential to enable a set of applications that help avoid traffic accidents. The performance of these applications, largely affected by the reliability of communication links, stringently ties back to the MAC and PHY layer design, which has been standardized as IEEE 802.11p. The link reliabilities depend on the signal-to-interference-plus-noise ratio (SINR), which, in turn, depends on the locations and transmit power values of the transmitting nodes. Hence, an accurate network model needs to take into account the network geometry. For such geometric models, however, there is a lack of mathematical understanding of the characteristics and performance of IEEE 802.11p. Important questions such as the scalability performance of IEEE 802.11p have to be answered by simulations, which can be very time consuming and provide limited insights to future protocol design. In this paper, we investigate the performance of IEEE 802.11p by proposing a novel mathematical model based on queuing theory and stochastic geometry. In particular, we extend the Matérn hard-core type-II process with a discrete and nonuniform distribution, which is used to derive the temporal states of backoff counters. By doing so, concurrent transmissions from nodes within the carrier sensing ranges of each other are taken into account, leading to a more accurate approximation to real network dynamics. A comparison with Network Simulator 2 (ns2) simulations shows that our model achieves a good approximation in networks with different densities.

publication date

  • May 1, 2016

published in

Digital Object Identifier (DOI)

start page

  • 1448

end page

  • 1458

volume

  • 17

issue

  • 5