Channel modeling for V2V communication: Enhancing road safety and intelligent transportatio

Abstract

The advancement of the Internet of Things (IoT) and Vehicle-to-Vehicle (V2V) communication networks has led to increased research efforts toward developing efficient and innovative V2V communication systems. These systems are designed to achieve the goal of improving road safety and enabling intelligent transportation. A critical aspect of this effort is channel modeling. In this context, this paper summarizes the characteristics of V2V communication channels. It categorizes them into three main types: Deterministic models, Non-geometry stochastic models (NGSMs), and Geometry-based stochastic models (GBSMs). It then reviews recent literature on these three types of channel modeling methods and discusses future development directions and challenges for V2V channel modeling. The study highlights the importance of accurate channel modeling for successful V2V communication systems and emphasizes the need to consider the unique features of V2V channels. It also identifies the limitations of existing channel models and suggests areas of research that can lead to more reliable and efficient V2V communication systems.