Vehicular Visible Light Communications Noise Analysis and Autoencoder Based Denoising

Abstract

Vehicular visible light communications (V-VLC) is a promising intelligent transportation systems (ITS) technology for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications with the utilization of light emitting diodes (LEDs). The main degrading factor for the performance of V-VLC systems is noise. Unlike traditional radio frequency (RF) based systems, V-VLC systems include many noise sources: solar radiation, background lighting from vehicle, street, parking garage and tunnel lights. Traditional V-VLC system noise modeling is based on the additive white Gaussian noise assumption in the form of shot and thermal noise. 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. We also propose a generalized Wiener process based V-VLC channel noise synthesis methodology to generate different noise components. We further propose convolutional autoencoder (CAE) based denoising scheme to reduce V-VLC signal noise, which achieves reconstruction root mean square error (RMSE) of 0.0442 and 0.0474 for indoor and outdoor channels, respectively.