Models to generate mobility based on the Bayesian approach for Vehicular Ad-hoc Networks (VANETs)

Vehicular Ad-hoc Networks (VANETs) are distributed, self-organizing communication networks built up by moving vehicles, and are thus characterized by a very high node mobility and limited degrees of freedom in the mobility patterns. Such particular features often make standard networking protocols inefficient or unusable in VANETs. In the past, the wireless networking community relied on simple models such as random waypoint . However, this model has found to be too simplistic although very useful in analysis and simulation. The latter, on the other hand was widely accepted and used in simulations. However, recently the researcher has started to focus on the alternative mobility models with different mobility characteristics. This paper is focusing on the development of accurate models based on the Bayesian approach describing the random movement of nodes in the group of Random-based Mobility Model in mobile wireless networks. The Bayesian approach to adaptive filtering exploits the a priori information in a stationary parameter variation model to optimize adaptive filtering performance. The prior information contains two critical parameter characteristics: the variance (magnitude) of the various filter coefficients and their variation spectrum (power delay profile and Doppler spectrum in the case of wireless channel tracking). The practical tool for implementing Bayesian Adaptive Filtering (BAF) is the Kalman filter, which typically models the parameter variation as an AR(1) process. To further limit the complexity to the same order as the complexity of the RLS algorithm, a diagonal AR(1) model can be taken. The hyperparameters in the resulting state model can be estimated with the EM approach. In this paper, we analyze the effect of power delay profile and Doppler bandwidth on the steady-state performance of BAF and LMS and RLS algorithms. The approximation effects of using a simplified state model are also exhibited.