Abstract
Network level modeling of vehicular networks usually takes one of two paths. Either a mobility simulator is used to
generate vehicular movement traces, combined with a network
simulator to simulate packet transmissions. Or, simple stochastic
assumptions, such as Poisson Point Processes and Manhattan
Grids are imposed to allow analytical modeling. In this paper,
we use the combination of mobility and network simulations
to derive more accurate analytical models for vehicular ad-hoc
networks in dense urban scenarios. Our results show that cars
tend to group in clusters with approximately exponential geometric densities. Furthermore, we demonstrate that the process
of interference in a dense network can be accurately modeled
based on a linear function of the numbers of neighbors, as well
as a Gamma distributed random process.
generate vehicular movement traces, combined with a network
simulator to simulate packet transmissions. Or, simple stochastic
assumptions, such as Poisson Point Processes and Manhattan
Grids are imposed to allow analytical modeling. In this paper,
we use the combination of mobility and network simulations
to derive more accurate analytical models for vehicular ad-hoc
networks in dense urban scenarios. Our results show that cars
tend to group in clusters with approximately exponential geometric densities. Furthermore, we demonstrate that the process
of interference in a dense network can be accurately modeled
based on a linear function of the numbers of neighbors, as well
as a Gamma distributed random process.
| Original language | English |
|---|---|
| Title of host publication | 2019 13th European Conference on Antennas and Propagation (EuCAP) |
| Publisher | IEEE Computer Society |
| Pages | 1-5 |
| Number of pages | 5 |
| ISBN (Electronic) | 978-88-907018-8-7 |
| ISBN (Print) | 978-88-907018-8-7 |
| Publication status | Published - 2019 |
| Externally published | Yes |
Publication series
| Name | 2019 13th European Conference on Antennas and Propagation (EuCAP) |
|---|