3D non-stationary GBSMs for high-speed train tunnel channels

Yu Liu, Liu Feng, Jian Sun, Wensheng Zhang, Cheng-Xiang Wang, Pingzhi Fan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper proposes 3D non-stationary multipleinput multiple-output (MIMO) geometry-based stochastic models (GBSMs) for high-speed train (HST) tunnel channels. Considering the line-of-sight (LoS), single-bounced (SB), and double-bounced (DB) components from the geometrical tunnel scattering model, a reference HST tunnel channel model under the assumption that scatterers are uniformly distributed on the tunnel walls is first derived. Then, by using the modified method of equal areas (MMEA), the corresponding simulation model is developed. Based on the proposed tunnel channel models, the correlation properties in time and space domains are investigated. A good agreement of statistical properties between the reference model and simulation model can be obtained. Furthermore, the simulation results show that the proposed model can be applied to mimic the nonstationarity of HST tunnel channels.

Original languageEnglish
Title of host publication2018 IEEE 87th Vehicular Technology Conference (VTC Spring)
PublisherIEEE
Pages1-5
Number of pages5
ISBN (Electronic)9781538663554
DOIs
Publication statusPublished - 26 Jul 2018

Publication series

NameIEEE Vehicular Technology Conference
PublisherIEEE
ISSN (Electronic)2577-2465

Keywords

  • channel model
  • High-speed train
  • statistical properties
  • tunnel scenario

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics

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  • Cite this

    Liu, Y., Feng, L., Sun, J., Zhang, W., Wang, C-X., & Fan, P. (2018). 3D non-stationary GBSMs for high-speed train tunnel channels. In 2018 IEEE 87th Vehicular Technology Conference (VTC Spring) (pp. 1-5). (IEEE Vehicular Technology Conference). IEEE. https://doi.org/10.1109/VTCSpring.2018.8417802