Ultra-wideband Channel Model for Intra-vehicular Wireless Sensor Networks Beneath the Chassis: From Statistical Model to Simulations

Intra-vehicular wireless sensor networks (IVWSNs) are 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 tires. The most suitable technology that can meet the high reliability requirement of vehicle control systems and the strict energy efficiency requirement of the sensor nodes in such harsh environment containing a large number of metal reflectors at short distance is the ultra-wideband (UWB). However, there are currently no detailed models describing the UWB channel for IVWSNs, making it difficult to design a suitable communication system. We analyze the small-scale and large-scale statistics of the UWB channel beneath the chassis of a vehicle by collecting data at various locations with 81 measurement points per transmitter-receiver pair for different types of vehicles, including the scenarios of turning the engine on and movement on the road. Collecting multiple measurements allows us to both improve the accuracy of the large-scale fading representation and model small-scale fading characteristics. The path-loss exponent around the tires and other locations beneath the chassis are found to be very different, requiring separate models. The power variation around the path loss has lognormal distribution. The clustering phenomenon observed in the averaged power delay profile (PDP) is well characterized by the Saleh-Valenzuela (SV) model. The cluster amplitude and decay rate are formulated as a function of the cluster arrival times using dual-slope linear models. Cluster interarrival times are modeled using Weibull distribution, providing a better fit than the commonly used exponential distribution in the literature, mainly due to the nonrandomness of the local structure of the vehicle. The variations of local PDPs around the small-scale averaged (SSA) PDPs, in decibels, at each delay bin are modeled by Gaussian distribution with variance independent of the value of the delay and distance between the transmitter and the receiver. The analysis of the model parameters for different vehicles and different scenarios demonstrates the robustness of our modeling approach exhibiting small variance in channel parameters for different vehicle types. Finally, the algorithm for generating the channel model is given. The generated PDPs are in good agreement with the experimental profiles, validating our model.