Visible Light Communication Channel Modeling Using Variational Autoencoders with Real-World Data

This paper explores the application of artificial neural networks in Visible Light Communication (VLC) technology, addressing the gap in channel modeling for light-emitting diodes (LEDs). Using a prototype emitter/receiver setup, we collected real-world data to train a Variational Autoencoder (VAE). The VAE simulates the VLC channel as a black box, capturing its behavior with high fidelity. Our results demonstrate that the VAE closely replicates the real VLC channel, with an average Error Vector Magnitude (EVM) difference of approximately 1% compared to actual measurements. These findings indicate that VAEs can effectively model VLC channels, providing accurate simulations that closely match real-world data.