Propagation Models in Vehicular Communications Academic Article in Scopus uri icon

abstract

  • © 2013 IEEE.In the advent of becoming reality, the era of autonomous vehicles is closer than ever, and with it, the need for faster and reliable wireless connections. The propagation channel determines the performance limits of wireless communications, and with the aid of empirical measurements, channel modeling is the best approach to predict and recreate how signal propagation conditions may perform. To this end, many different approaches and techniques have been implemented, from specific applications to general models, considering the characteristics of the environment (geometry-based or non-geometry-based) as well as seeking high performance algorithms in order to achieve good balance between accuracy and computational cost. This paper provides an updated overview of propagation channel models for vehicular communications, beginning with some specific propagation characteristics of these complex heterogeneous environments in terms of diverse communication scenarios, different combinations of link types, antenna placement/diversity, potentially high Doppler shifts, or non-stationarity, among others. The presented channel models are classified in four categories: empirical, non-geometry-based stochastic, geometry-based stochastic, and deterministic models, following the classical approach. The features and key concepts of the different vehicular communications channel models are presented, from sub 6 GHz to millimeter wave (mmWave) frequency bands. The advantages and disadvantages of the main works in the area are discussed and compared in a comprehensive way, outlining their contributions. Finally, future critical challenges and research directions for modeling reliable vehicular communications are introduced, such as the effects of vegetation, pedestrians, common scatterers, micro-mobility or spherical wavefront, which in the context of the near future are presented as research opportunities.

publication date

  • January 1, 2021