A 3D Non-Stationary GBSM for Vehicular Visible Light Communication MISO Channels

TL;DR

This paper introduces a novel 3D stochastic model for vehicular VLC MISO channels that accounts for channel impairments, analyzes the relationship between range and power, and evaluates system SNR considering background noise.

Contribution

It proposes a new 3D RS-GBSM combining two-sphere and elliptic-cylinder models, incorporating azimuth and elevation angles with VMF distribution for vehicular VLC channels.

Findings

The 3D model accurately predicts received optical power over range.

Elevation angle significantly impacts received power compared to 2D models.

Background noise modeling enables SNR evaluation for system performance.

Abstract

The potential of using visible light communication (VLC) technologies for vehicular communication networks has recently attracted much attention. The underlying VLC channels, as a foundation for the proper design and optimization of vehicular VLC communication systems, have not yet been sufficiently investigated. Vehicular VLC link impairments can have a significant impact on the system performance and capacity. Such impairments include the optical wireless channel distortion and background noise. This paper proposes a novel three-dimensional (3D) regular-shaped geometry-based stochastic model (RS-GBSM) for vehicular VLC multiple-input single-output (MISO) channels. The proposed 3D RS-GBSM combines a two-sphere model and an elliptic-cylinder model. Both the line-of-sight (LoS) and single-bounced (SB) components are considered. The proposed model jointly considers the azimuth and elevation angles by using von-Mises-Fisher (VMF) distribution. Based on the proposed model, the relationship between the communication range and the received optical power is analyzed and validated by simulations. The impact of the elevation angle in the 3D model on the received optical power is investigated by comparing with the received optical power of the corresponding two-dimensional (2D) model. Furthermore, the background noise is also modeled to evaluate the system's signal-to-noise ratio (SNR).