MmWave channel model for intra-vehicular wireless sensor networks

Abstract

Intra-vehicular wireless sensor networks (IVWSNs) have significant potential to reduce part, manufacturing and repair costs, facilitate the integration of new nodes and provide more freedom for the sensor placement in previously impossible locations by obviating the need for wiring harness. mmWave stands up as a promising candidate to fulfill the high reliability, security and low latency requirements of IVWSNs, exploiting the availability of large bandwidth at high frequencies and high nominal gains with directional antennas while attenuating through the car body significantly. This work focuses on building a mmWave channel model for IVWSNs. The model is built for the engine compartment, passenger compartment and beneath the chassis of a vehicle by conducting vast number of measurements for 14 × 14, 13 × 13 and 15 × 15 transmitter and receiver links in a Fiat Linea, respectively. The path loss exponent is approximately 3, showing almost no variation within different compartments. The power variation around the path loss model has a Generalized Extreme Value (GEV) distribution with zero mean for all compartments and 5 dB standard deviation for the engine compartment and approximately 7.6 dB standard deviation for the other two compartments. A modified Saleh–Valenzuela (SV) model is used to represent the clustering of power delay profiles (PDPs). Log-normal distribution is used to model the inter-arrival times of clusters, while the dependencies of cluster amplitude and ray decay rate on the cluster arrival times are represented by a dual slope linear fit model with breakpoints 1.2 ns and 5.6 ns for engine compartment, respectively, and 1.6 ns and 2.6 ns for the other two compartments, respectively. The experimental PDPs vary around the SV model and these variations are represented by a normal distribution with zero mean and 5.8 dB standard deviation, which is independent of both the delay bins and the compartment of the vehicle. All these findings are used to build a simulation model for each compartment of the vehicle. The simulation model is validated by comparing the distributions of the received powers and Root Mean Square (RMS) delay spreads of the experimental and simulated PDPs.