A Geometry-Based Stochastic Model for Truck Communication Channels in Freeway Scenarios

TL;DR

This paper introduces a geometry-based stochastic model for truck-related vehicle-to-vehicle communication channels in freeway scenarios, validated with extensive measurements to improve realistic modeling for intelligent transportation systems.

Contribution

It proposes a novel hybrid GBSM specifically for T2X channels in freeways, with detailed parameterization and validation based on real-world measurements.

Findings

Model accurately predicts delay and angular spreads

Clusters classified into LOS, static, mobile, and diffuse scattering

Validation shows good agreement with measurement data

Abstract

Vehicle-to-vehicle (V2V) wireless communication systems are fundamental in many intelligent transportation applications, e.g., traffic load control, driverless vehicle, and collision avoidance. Hence, developing appropriate V2V communication systems and standardization require realistic V2V propagation channel models. However, most existing V2V channel modeling studies focus on car-to-car channels; only a few investigate truck-to-car (T2C) or truck-to-truck (T2T) channels. In this paper, a hybrid geometry-based stochastic model (GBSM) is proposed for T2X (T2C or T2T) channels in freeway environments. Next, we parameterize this GBSM from the extensive channel measurements. We extract the multipath components (MPCs) by using a joint maximum likelihood estimation (RiMAX) and then cluster the MPCs based on their evolution patterns.We classify the determined clusters as line-of-sight, multiple-bounce reflections from static interaction objects (IOs), multiple-bounce reflections from mobile IOs, multiple-bounce reflections, and diffuse scattering. Specifically, we model multiple-bounce reflections as double clusters following the COST 273/COST2100 method. This article presents the complete parameterization of the channel model. We validate this model by contrasting the root-mean-square delay spread and the angular spreads of departure/arrival derived from the channel model with the outcomes directly derived from the measurements.