A Tractable Stochastic Channel Model for V2X Communications

Intelligent Transportation Systems (ITS) and Vehicle-to-everything (V2X) communications are two major fields of growing interest. Among the numerous challenges in V2X performance evaluation, modeling the communication channel is a vital one. Due to the nature of high mobility and the dynamic topology of V2X networks, defining a channel model to capture the real-time characteristics of V2X environments is of paramount importance to efficiently developing, designing, and optimizing novel Radio Access Technologies (RATs) that are capable of achieving a very high data capacity to support vehicular communications and to meet the demanding Quality-of-Service (QoS) requirements of ultra-reliable applications.In this paper, a stochastic channel model is proposed that agrees with the above-mentioned unique characteristics of V2X environments. One major advantage of the presented model is its tractability, which provides us with opportunities to leverage the use of adaptive tracking techniques toward the prediction and estimation of the time-varying V2X channel behavior. Channel prediction and estimation are central to the entire discipline of channel modeling, which aims to make more accurate decisions about the operation of the vehicular network under consideration. Traffic and system parameters found to be influencing channel modeling and estimation have been extensively explored in simulations. The findings of the simulation scenario proved the validity of the channel model and confirmed an improved performance for the tracking and estimation technique employed on the presented channel model.