Performance prediction of visible light communication system using machine learning: a comparative analysis of classifiers
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Prabhjot Kaur
,
Rajandeep Singh
and
Gurpreet Kaur
Abstract
The performance of a VLC system depends heavily on various parameters, such as link distance, transmitter and irradiance half-angles, detection surface area, optical concentration factor, and incidence angle. However, predicting the system’s performance in real-world conditions based on combinations of these parameters is challenging. To address this, this paper proposes a machine learning-based prediction model that assesses the feasibility of VLC system configurations using known parameter values. A synthetic dataset was generated through the optical simulation software Optisystem, and multiple machine learning algorithms were trained on this data. The classifiers, including multilayer perceptron (MLP), random forest classifier (RFC), logistic regression (LR), naïve Bayes (NB), decision tree (DTR), and K-nearest neighbors (K-NN), were compared for this task. Results show that both MLP and RFC (with 50 estimators) achieved the highest accuracy of 95 %. However, MLP is preferred due to its superior cross-validation score of 84 and an ROC AUC of 98, making it the most effective model for predicting VLC system performance.
Acknowledgements
We wish to thank the APON Lab of Punjabi University, Patiala, for providing the computational system for the training and testing of the AI models of this work.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: None declared.
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Data availability: Not applicable.
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