Machine-Learning-Based Data Processing Techniques for Vehicle-to-Vehicle Channel Modeling

Machine learning has recently drawn much attention for a variety of applications, thanks to its good performance in identification, recognition, and regression problems. One such important application is V2V communication propagation channel research. In this article, the challenges and opportunities of machine-learning-based data processing techniques for evaluation of V2V channel measurements are presented. This article reviews some state-of-the-art applications including identification of channel line-of-sight situations, tracking of MPCs, and MPC clustering. The data obtained with these methods form, inter alia, the basis for accurate channel models. Furthermore, some challenges of machine-learning-based data processing for V2V channel research are discussed as basis for future studies.