Abstract-Infrastructure-based vehicular networks (consisting of a group of Base Stations (BSs) along the road) will be widely deployed to support Wireless Access in Vehicular Environment (WAVE) and a series of safety and non-safety related applications and services for vehicles on the road. As an important measure of user satisfaction level, uplink connectivity probability is defined as the probability that messages from vehicles can be received by the infrastructure (i.e., BSs) through multi-hop paths. While on the system side, downlink connectivity probability is defined as the probability that messages can be broadcasted from BSs to all vehicles through multi-hop paths, which indicates service coverage performance of a vehicular network. This paper proposes an analytical model to predict both uplink and downlink connectivity probabilities. Our analytical results, validated by simulations and experiments, reveal the trade-off between these two key performance metrics and the important system parameters, such as BS and vehicle densities, radio coverage (or transmission power), and maximum number of hops. This insightful knowledge enables vehicular network engineers and operators to effectively achieve high user satisfaction and good service coverage, with necessary deployment of BSs along the road according to traffic density, user requirements and service types. Index Terms-Vehicular ad-hoc Network (VANET), Wireless Access in Vehicular Environment (WAVE), IEEE 802.11p, IEEE 1609, Connectivity Probability
In REF , Zhang et al. proposed a mathematical model to analyze both multihop uplink and downlink connectivity for infrastructure-based vehicular networks, where uplink connectivity probability indicates the user satisfaction level and downlink connectivity probability represents service coverage performance of a vehicular network.
Multi-Hop Connectivity Probability in Infrastructure-Based Vehicular Networks
IEEE Journal on Selected Areas in Communications
IEEE Journal on Selected Areas in Communications
Computer Science