Modeling of time-variant scattering fields for vehicle-to-vehicle channels

Kurzfassung

Wireless vehicle-to-vehicle communication systems are a crucial part of intelligent transportation systems as they shall ensure the in-time delivery of various types of sensor data. This data is collected by individual vehicles for the purpose of e.g. collision avoidance or traffic flow optimization. Profound understanding of wireless vehicle-to-vehicle propagation channels is essential to develop efficient and robust communication systems, which provide fast information distribution and access. Thus, accurate channel models, which reproduce the vehicle-to-vehicle propagation environment in a realistic manner, are required. In this paper, we derive an exact mathematical description of the time-variant local scattering functions for vehicular environments. Previously reported measurements of vehicle-to-vehicle channels have uncovered their non-stationarity; therefore the wide-sense stationary, uncorrelated scattering assumption for such channels is often violated. This fact makes their modeling challenging. In our approach we use geometry-based stochastic modeling to cope with the non-stationarity of vehicle-to-vehicle channels. For this reason a local delay-dependent Doppler probability density function is derived for a fixed time and a fixed carrier frequency. We consider a highway scenario where it is assumed that scatterers are randomly distributed on scatter belts along both sides of the road. In contrast to the classical, stochastic modeling approaches, we consider the exact geometry of the scenario. In several steps throughout our mathematical analysis we identify scatterers with equal delay. The respective Doppler probability function depends on the size of the scatterer belts and the two moving cars. This approach leads to the delay-dependent Doppler probability density function in vehicle-to-vehicle propagation environments; furthermore, the computations of the probability density functions can be performed numerically without computationally expensive ray tracing or Monte Carlo methods. By combining the obtained Doppler profiles with a delay probability density function, the local scattering function can then be obtained for different time instances. This allows capturing the non-stationary behavior of the vehicular channel. The paper demonstrates the benefits of the proposed approach by computing a time series of local scattering functions for two vehicles driving in opposite directions on a highway. The presented modeling approach is simple yet very general and accurate, which allows its application in different vehicular communication scenarios.