A Novel 3D Non-Stationary Vehicle-to-Vehicle Channel Model and Its Spatial-Temporal Correlation Properties

Qiuming Zhu, Ying Yang, Xiaomin Chen, Yi Tan, Yu Fu, Cheng-Xiang Wang, Weidong Li

Research output: Contribution to journalArticlepeer-review

27 Citations (Scopus)
95 Downloads (Pure)

Abstract

In this paper, a new non-stationary Vehicle-to-Vehicle (V2V) channel model is proposed. It could generate more smooth fading phase between adjacent channel states and guarantee more accurate Doppler frequency, which is a great improvement comparing with those of the existing non-stationary geometry based stochastic models (GBSMs) for V2V channels. Meanwhile, the spatial-temporal correlation function (STCF) as well as temporal correlation function (TCF) and spatial correlation function (SCF) are derived in details based on the power angle spectrums (PASs) of both the mobile transmitter (MT) and mobile receiver (MR) following the Von Mises Fisher (VMF) distribution. Simulation results have demonstrated that the time-variant correlation properties of our proposed channel model have an excellent agreement with the theoretical results, which verifies the correctness of theoretical derivations and simulations. Finally, the TCF and stationary interval of the proposed model are verified by the measured results.

Original languageEnglish
Pages (from-to)43633-43643
Number of pages11
JournalIEEE Access
Volume6
Early online date27 Jul 2018
DOIs
Publication statusPublished - 2018

Keywords

  • Azimuth
  • Channel models
  • Correlation
  • Doppler effect
  • geometry-based stochastic model (GBSM)
  • non-stationary Vehicle-to-Vehicle (V2V) channel
  • spatial-temporal correlation properties
  • Three-dimensional displays
  • Vehicular ad hoc networks
  • Von Mises Fisher (VMF) distribution
  • Zinc

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering

Fingerprint

Dive into the research topics of 'A Novel 3D Non-Stationary Vehicle-to-Vehicle Channel Model and Its Spatial-Temporal Correlation Properties'. Together they form a unique fingerprint.

Cite this