Machine Learning and Deep Learning in VLC Systems: A Comprehensive Survey

Visible light communication (VLC) has emerged as a promising wireless communication technology, utilizing light-emitting diodes for high-speed data transmission. While VLC offers advantages such as high bandwidth, energy efficiency, and enhanced security, it faces several limitations, including noise, environmental interference, signal blockage, and the requirement for a direct line of sight. Machine learning (ML) and deep learning (DL) have been widely explored in VLC applications to address these challenges and improve system adaptability. This survey comprehensively reviews ML and DL techniques in VLC systems, focusing on key areas such as channel estimation, noise mitigation, modulation classification, and symbol detection. Furthermore, the contributions of DL in advanced VLC systems, such as intelligent reflecting surface-assisted MIMO and non-orthogonal multiple access-aided VLC systems, are described. Additionally, this review highlights the role of ML and DL in hybrid VLC/RF systems, focusing on resource management and dynamic network selection. The details of DL-based localization for indoor VLC systems are also discussed. The implementation of DL in image sensor-based VLC systems is highlighted for both indoor and outdoor vehicular applications. Four major search engines—Google Scholar, Science Direct, Web of Science, and IEEE Xplore—were used to identify and evaluate quantitative studies. This review emphasizes five key aspects: model architectures, modulation techniques, channel models, model performance, and the contributions or applications of each algorithm. Finally, we highlight future research directions and unresolved challenges, emphasizing the integration of ML/DL for security in the VLC systems, intelligent networking, and real-time adaptive control. This survey aims to provide valuable insights into the role of ML and DL in enhancing the efficiency and reliability of next-generation VLC systems.