Ultra-Wideband Channel Model for Intra-Vehicular Wireless Sensor Networks

Intra-vehicular wireless sensor networks is a promising new research area that can provide part cost, assembly, maintenance savings and fuel efficiency through the elimination of the wires, and enable new sensor technologies to be integrated into vehicles, which would otherwise be impossible using wired means, such as Intelligent Tire. The most suitable technology that can meet high reliability, strict energy efficiency and robustness requirements of these sensors in such a harsh environment at short distance is Ultra-Wideband (UWB). However, there are currently no detailed models describing the UWB radio channel for intra-vehicular wireless sensor networks making it difficult to design a suitable communication system. We analyze the small-scale and large-scale statistics of the UWB channel based on a measurement campaign for a variety of sensor locations beneath the chassis of a vehicle. The analysis for large-scale statistics show that the characteristics of the channel around the tires is very different from the other parts under the chassis. The path loss exponents around the tires and under chassis are 4 and 2.2 respectively. The clustering phenomenon observed in the averaged power delay profile can be well-modeled by Saleh-Valenzuela model. The clusters decay exponentially with arrival time but with a smaller decay constant after 30ms. The decay rate of ray amplitudes is increasing with delay and can be modeled using a dual slope linear model in logarithmic scale. The best fit for inter-cluster arrival time is Weibull distribution. The analysis for small-scale statistics on the other hand show that the best fit for the received energies in each bin at 81 locations of the measurement grid is lognormal distribution with decreasing mu and almost constant sigma parameters. Moreover, different bins of the delay can be assumed to fade independently. This is the first work to model small-scale channel characteristics for intravehicular wireless sensor networks.