Vehicular Visible Light Communications Noise Analysis and Autoencoder Based Denoising

TL;DR

This paper analyzes noise in vehicular visible light communications using Allan variance, and introduces a convolutional autoencoder for effective noise reduction, improving signal clarity in V-VLC systems.

Contribution

It presents a novel noise analysis method with Allan variance and a CAE-based denoising scheme tailored for V-VLC channels, addressing complex noise sources.

Findings

Noise analysis with Allan variance distinguishes time-correlated noise

Proposed autoencoder achieves low RMSE in noise reduction

Enhanced signal quality in indoor and outdoor V-VLC channels

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 vehicles, streets, parking garages, 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 a 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.