Machine Learning Based Channel Modeling for Vehicular Visible Light Communication

no code implementations • 3 Feb 2020 • Bugra Turan, Sinem Coleri

Current OWC channel models based on deterministic and stochastic methods, fail to address mobility induced ambient light, optical turbulence and road reflection effects on channel characterization.

BIG-bench Machine Learning Ensemble Learning

Vehicular Visible Light Communications Noise Analysis and Autoencoder Based Denoising

no code implementations • 20 Nov 2021 • Bugra Turan, O. Nuri Koc, Emrah Kar, Sinem Coleri

In this paper, to investigate both time-correlated and white noise components of the V-VLC channel, we propose a noise analysis based on Allan variance (AVAR), which provides a time-series analysis method to identify noise from the data.

Denoising Time Series +1

Measurement Based Non-Line-Of-Sight Vehicular Visible Light Communication Channel Characterization

no code implementations • 22 Nov 2021 • Bugra Turan, Omer Narmanlioglu, Osman Nuri Koc, Emrah Kar, Sinem Coleri, Murat Uysal

We propose a distance-based NLoS V-VLC channel path loss model considering reflection surface characteristics and NLoS V-VLC channel impulse response (CIR) incorporating the temporal broadening effect due to vehicle reflections through weighted double gamma function.