A computationally inexpensive empirical model of IEEE 802.11p radio shadowing in urban environments

Abstract

Abstract-We present a realistic, yet computationally inexpensive simulation model for IEEE 802.11p radio shadowing in urban environments. Based on real world measurements using IEEE 802.11p/DSRC devices, we estimated the effect that buildings and other obstacles have on the radio communication between vehicles. Especially for evaluating safety applications in the field of Vehicular Ad Hoc Networks (VANETs), stochastic models are not sufficient for evaluating the radio communication in simulation. Motivated by similar work on WiFi measurements, we therefore created an empirical model for modeling buildings and their properties to accurately simulate the signal propagation. We validated our model using real world measurements in a city scenario for different types of obstacles. Our simulation results show a very high accuracy when compared with the measurement results, while only requiring a marginal overhead in terms of computational complexity.

Coherent, automatic address resolution for vehicular ad hoc networks

“…We used only the Best Effort (BE) access category in the MAC layer. We used an empirical model of radio shadowing (Sommer et al, 2011) in IEEE 802.11p networks as path loss model. Also, as fading model we used Rician when vehicles are in line of sight (LOS) and Rayleigh when vehicles are not in LOS (Rappaport, 2001).…”

Localized Power Control for Multihop Large-Scale Internet of Things

“…We have selected R o = 30 meters, which is the typical separation between two neighboring residential or mid-size buildings. We also know from [36] that the path loss exponent α has a profound impact on the wireless link. As such, we run the computation for α ∈ [2.7 − 3.5] which is a typical range for urban environments [37].…”

Scheduling in green vehicular infrastructure with multiple roadside units

“…The input data to the schedulers is taken from a highway environment where vehicles are assumed to maintain constant speed throughout the RSU coverage areas (Khabazian and Ali, 2008) (Hammad et al, 2013). It has been shown that in this type of scenario, good estimates of energy costs can be readily made (Wang, 2005)(C. Sommer and Dressler, 2011). The associated energy cost inputs are based on vehicle position and associated estimates of downlink transmission energy costs.…”