AI-Augmented Visible Light Communication: A Framework for Noise Mitigation and Secure Data Transmission

TL;DR

This paper introduces an AI deep learning framework that enhances visible light communication by mitigating noise and improving data security, demonstrated through simulation and BER performance improvements.

Contribution

The paper proposes a novel AI-based deep neural network model for noise mitigation in VLC systems, improving signal quality and security over traditional methods.

Findings

Deep neural network effectively reduces BER in VLC systems

Simulation shows improved signal integrity with AI-based equalization

Framework enhances noise resilience and data security in VLC

Abstract

This paper presents a proposed AI Deep Learning model that addresses common challenges encountered in Visible Light Communication (VLC) systems. In this work, we run a Python simulation that models a basic VLC system primarily affected by Additive White Gaussian Noise (AWGN). A Deep Neural Network (DNN) is then trained to equalize the noisy signal received and improve signal integrity. The system evaluates and compares the Bit Error Rate (BER) before and after equalization to demonstrate the effectiveness of the proposed model. This paper starts by introducing the concept of visible light communication, then it dives deep into some details about the process of VLC and the challenges it faces, shortly after we propose our project which helps overcome these challenges. We finally conclude with a lead for future work, highlighting the areas that are most suitable for future improvements.