Pedestrian-to-Vehicle Communications in an Urban Environment: Channel Measurements and Modeling

As wireless connectivity becomes increasingly ubiquitous, a greater emphasis will be placed upon the seamless integration of dissimilar networking technologies. One such example of this will occur in urban environments, where wearable devices and vehicular networks will operate in close proximity to one another. Clearly, a natural extension to both types of network is their interconnectivity through vehicle-to-pedestrian (V2P) or equivalently pedestrian-to-vehicle (P2V) communications as part of a much greater vehicle-to-X (V2X) based Intelligent Transportation System (ITS). To this end, we empirically investigate the P2V communications channel at 5.8 GHz for the case of a moving vehicle when a person positioned by the edge of a road was either stationary or walking parallel to the side of the highway. The measurements considered a chest mounted transmitter and four receiver locations on the vehicle covering the front wing mirrors and two positions on the roof, which simultaneously recorded the received signal power. To characterize the propagation mechanisms which are responsible for shaping the received signal in the P2V channel we decomposed it into its path loss, large-scale and small-scale fading components. We first show that although there was evidence of interference caused by multiple rays interacting with one another, the popular Two- Ray ground-reflection path loss model was unable to adequately describe the compounded effects of the vehicle and pedestrian’s body on the signal attenuation in the majority of the considered scenarios. Instead, we found that the overall path loss was well characterized using a dual-slope log-distance model, with lognormal large-scale fading. Due to the often severe small-scale fading that was observed in the P2V channel, we have been able to utilize the κ-μ Extreme distribution with considerable success to characterize the worse than Rayleigh fading conditions which were encountered.