Machine Learning in Visible Light Communication System: A Survey

Abstract

With the widespread adoption of high bandwidth utilisation, visible light communication (VLC) has emerged as a potential solution to meet the demands for high-speed data communication due to its simultaneous illumination and transmission. However, numerous nonlinear distortions in VLC cause substantial signal processing challenges and diminish the system’s efficacy. VLC communication based on machine learning (ML) approaches provides a greater ability to offset the negative impacts of transceiver nonlinearity. ML is applicable to a variety of VLC challenges, including channel estimation, jitter compensation, position tracking, modulation detection, phase estimation, and security. This study provides a detailed review of several machine learning (ML) algorithms to reduce the design complexity of indoor VLC transmission, as well as ML applications in different design aspects to improve system performance. Furthermore, various applications, challenges, and future research directions based on machine learning algorithms in VLC are addressed.