Abstract
Visible light communication (VLC) has emerged as a new and promising technology in
wireless communications. Its ability to provide illumination, energy transfer, and data
transmission through light-emitting diodes (LEDs) makes it a valuable innovation for
next-generation communication systems. Among its multiple advantages are high data
transfer speeds, reduced interference with radio frequency signals, improved security in
the indoor environment, low cost and complexity, and lower hardware investment. However,
despite these benefits, VLC systems are hindered by nonlinear channels, which have
a substantial impact on data transmission accuracy and rate.
In this thesis, by leveraging their capacity to solve nonlinear problems, machine learning
(ML) algorithms are integrated to the VLC system to address the challenges posed by
VLC channel nonlinearity and predict the received message. The work focuses on an indoor
VLC system with multiple inputs and outputs. Key aspects of the system, such as
LED placement, received power distribution, signal-to-noise ratio, and channel impulse
response due to LED configuration are analyzed.
We explored the application of various ML classification algorithms, including Random
Forest (RF), Decision Trees (DT), Support Vector Machine (SVM), and Na¨ıve Bayes
(NB) to enhance the indoor VLC system performance. Each algorithm’s performance is
evaluated. DT, RF, SVM, AND NB produced average precision accuracy of 76.47%,
74.01%, 77.21%, and 77.21%, and average cross-validation of 79.36%, 82.96%, 78.58%,
and 77.82%, highlighting the potential of ML in mitigating nonlinear challenges faced by
VLC systems and demonstrating major advancements while identifying areas for further
improvement.